Current Medical Imaging - Volume 20, Issue 1, 2024

In the end stage of kidney disease, abnormal levels of blood calcium, phosphorus, and parathyroid hormone lead to bone metabolism disorders, manifesting as osteoporosis or fibrocystic osteoarthritis. X-ray, CT, and MR are useful for detecting bone lesions in dialysis patients, but currently, computer vision has not yet been used for this purpose.

ResNet is a powerful deep CNN model, which has not yet been used to distinguish between the bones of dialysis patients and healthy people. Therefore, this study aimed to investigate the ability of the Resnet50 model to identify the bone of dialysis patients from normal bone.

CT images of 200 cases (100 dialysis patients and 100 healthy people aged 31-72 years with male:female ratio of 51:49) were randomly divided into the training and testing groups at the ratio of 8:2. The module of ‘torch’ was used to train the model of Resnet50 for the current task of image classification. In the test cohort, the accuracy, sensitivity, and specificity with hyper-parameter=0 were 60%, 65%, and 55%, respectively. When the hyper-parameter was 0.6 or 0.7 versus 0, the accuracy was significantly higher (P<0.05). When the hyper-parameter was another number, the accuracy was not significantly different from that with no hyper-parameter (P>0.05).

This study has indicated computer vision to be suitable for identifying bone changes caused by dialysis; a hyper-parameter has been found necessary for improving model accuracy. The ResNet50 model with hyper-parameter = 0.7 has exhibited 90% sensitivity in identifying the bone of dialysis patients.

Individuals with depression have an increased risk of cardiovascular disease, and more often have a poor prognosis with cardiovascular disease. This study aimed to investigate the impact of depression on Left Ventricular (LV) alterations using Cardiovascular Magnetic Resonance Feature-tracking (CMR-FT).

Seven anesthetized, healthy Chinese miniature swine were included in the study. Basic data, including CMR scans at baseline and after 14 days of depression modeling, were collected. Behavioral tests, including the Open-field Test (OFT), Sucrose Preference Test (SPT), and measurements of the time taken to consume a specific amount of food and sugar, were conducted to assess the success of the depression models. CMR cine images were acquired and CVI software was employed to analyze Global Longitudinal Strain (GLS), Global Circumferential Strain (GCS), and Global Radial Strain (GRS). Late Gadolinium Enhancement (LGE) imaging was used to detect myocardial infarction and/or scar.

The outcomes demonstrated successful depression modeling, indicated by reduced scores in the OFT and SPT, as well as an extended time to intake food and sugar compared to baseline. However, no significant differences were observed in LV End-diastolic Volume (LVEDV), LV End-systolic Volume (LVESV), LV Ejection Fraction (LVEF), LV End-diastolic Myocardial Mass (LVMASSED), and Cardiac Output (CO) before and after modeling. Regarding LV global strain parameters, there was a downward trend in GRS (25.35% ± 6.9% vs. 22.86% ± 6.4%, P=0.021), GCS (-16.71% ± 4.2% vs. -14.78% ± 2.3%, P=0.043), and GLS (-17.66% ± 2.9% vs. -14.53% ± 2.5%, P=0.056), respectively, after modeling. GRS and GCS were significantly reduced after modeling compared to baseline.

The study suggests that depression may contribute to early LV systolic dysfunction, particularly affecting LV GCS and GRS.

The metastasis of primary breast invasive lobular carcinoma to the gastrointestinal tract and skin is a rare phenomenon, with the simultaneous occurrence of both transfers being more uncommon.

This article reports a case of a patient with hormone receptor-positive, HER2-negative breast invasive lobular carcinoma with gastrointestinal tract and skin metastases. The patient was assessed by a second-look ultrasound and diagnosed by subsequent ultrasound-guided needle biopsy. Following endocrine therapy, a favorable effect was observed, with significant regression of the primary breast lesion, cutaneous metastases, and gastrointestinal metastases.

Patients with breast invasive lobular carcinoma should be alert to the possibility of breast cancer metastasis, even if there are no obvious symptoms or signs, when they encounter rapidly progressive cutaneous nodules or plaques, or if they possess gastrointestinal abnormalities. For patients with negative breast ultrasonography for the first time, after combining mammography, Contrast-enhanced Spectral Mammography (CESM) or Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) examinations, if breast cancer is highly suspected, second-look ultrasound is particularly crucial at this juncture, which is the key prerequisite for breast needle biopsy and obtaining the gold standard of pathology.

Carotid duplex ultrasonography (DUS) is the primary screening tool for carotid artery stenosis, but has low reliability. MHR, which is the ratio of monocytes to high-density lipoprotein cholesterol (HDL-C), can be a marker for the degree and distribution of extracranial and intracranial atherosclerotic stenosis.

We determined the diagnostic value of DUS+MHR for internal carotid artery (ICA) stenosis.

We divided 273 hospitalized patients into non-stenosis (<50%) and ICA stenosis (≥50%) groups based on Digital Subtraction Angiography (DSA). We determined the peak systolic velocity (PSV) in the ICA on DUS, calculated the MHR, and investigated their relationship with ICA stenosis.

On DSA, 34.1% (93/273) patients had moderate-to-severe ICA stenosis. DUS and DSA showed low concordance for detecting ICA stenosis (kappa = 0.390). With increasing age, the incidence of moderate-to-severe ICA stenosis increased. PSV, monocyte count, and MHR were significantly greater in the stenosis group than in the non-stenosis group (P < 0.001), while the HDL-C level was significantly lower (P = 0.001). PSV (OR: 1.020, 95% CI: 1.011–1.029, P < 0.001) and MHR (OR: 5.662, 95% CI: 1.945–16.482, P = 0.002) were independent risk factors for ICA stenosis. The area under the receiver operating characteristic curve of PSV+MHR (0.819) was significantly higher than that of PSV or MHR alone (77.42% sensitivity, P = 0.0207; 73.89% specificity, P = 0.0032).

The combination of ICA PSV on DUS and MHR is better than PSV alone at identifying ICA stenosis and is well-suited to screen high-risk patients.

Endometrial Cancer (EC) is a highly heterogeneous cancer comprising both histological and molecular subtypes. Using a non-invasive modality method to trigger these subtypes as early as possible can aid clinicians in establishing individualized treatment.

The study aimed to clarify the value of the Apparent Diffusion Coefficient (ADC) of EC MRI in determining molecular subtypes.

We retrospectively recruited 109 patients with pathologically proven EC (78 endometrioid cancers and 31 non-endometrioid cancers) with available molecular classification from a tertiary centre. MRI was prospectively performed a month prior to surgery; images were blindly interpreted by two experienced radiologists with consensus reading. The ADC value was measured by an experienced radiologist on the commercially available processing workstation. Interoperator measurement consistency was calculated.

Our sample comprised 17 PLOE, 32 MSI-H, 31 NSMP, and 29 P53abn ECs. Clinical information did not differ significantly among the groups. The maximum diameter and volume of the lesions differed among the groups. The ADC value in the maximal area (ADCarea) or region of interest (ROI, ADCroi) in the P53abn group was higher than that in the other groups (894.0 ±12.6 and 817.5 ± 83.3 x10-6 mm2/s). The ADC mean values were significantly different between the P53abn group and the other groups (P = 0.000). The nomogram showed the highest discriminative ability to distinguish P53abn EC from other types (AUC: 0.859).

Our results have suggested the quantitative MR characteristics (ADC values) derived from preoperative EC MRI to provide useful information in preoperatively determining P53abn cancer.

The incidence of stroke is rising, and it is the second major cause of mortality and the third leading cause of disability globally. The goal of this study was to rapidly and accurately identify carotid plaques and automatically quantify plaque burden using our automated tracking and segmentation US-video system.

We collected 88 common carotid artery transection videos (11048 frames) with a history of atherosclerosis or risk factors for atherosclerosis, which were randomly divided into training, test, and validation sets using a 6:3:1 ratio. We first trained different segmentation models to segment the carotid intima and adventitia, and calculate the maximum plaque burden automatically. Finally, we statistically analyzed the plaque burden calculated automatically by the best model and the results of manual labeling by senior sonographers.

Of the three Artificial Intelligence (AI) models, the Robust Video Matting (RVM) segmentation model's carotid intima and adventitia Dice Coefficients (DC) were the highest, reaching 0.93 and 0.95, respectively. Moreover, the RVM model has shown the strongest correlation coefficient (0.61±0.28) with senior sonographers, and the diagnostic effectiveness between the RVM model and experts was comparable with paired-t test and Bland-Altman analysis [P= 0.632 and ICC 0.01 (95% CI: -0.24~0.27), respectively].

Our findings have indicated that the RVM model can be used in ultrasound carotid video. The RVM model can automatically segment and quantify atherosclerotic plaque burden at the same diagnostic level as senior sonographers. The application of AI to carotid videos offers more precise and effective methods to evaluate carotid atherosclerosis in clinical practice.

Currently, it is difficult to find a solution to the inverse inappropriate problem, which involves restoring a high-resolution image from a low-resolution image contained within a single image. In nature photography, one can capture a wide variety of objects and textures, each with its own characteristics, most notably the high-frequency component. These qualities can be distinguished from each other by looking at the pictures.

The goal is to develop an automated approach to identify thyroid nodules on ultrasound images. The aim of this research is to accurately differentiate thyroid nodules using Deep Learning Technique and to evaluate the effectiveness of different localization techniques.

The method used in this research is to reconstruct a single super-resolution image based on segmentation and classification. The poor-quality ultrasound image is divided into several parts, and the best applicable classification is chosen for each component. Pairs of high- and low-resolution images belonging to the same class are found and used to figure out which image is high-resolution for each segment. Deep learning technology, specifically the Adam classifier, is used to identify carcinoid tumors within thyroid nodules. Measures, such as localization accuracy, sensitivity, specificity, dice loss, ROC, and area under the curve (AUC), are used to evaluate the effectiveness of the techniques.

The results of the proposed method are superior, both statistically and qualitatively, compared to other methods that are considered one of the latest and best technologies. The developed automated approach shows promising results in accurately identifying thyroid nodules on ultrasound images.

The research demonstrates the development of an automated approach to identify thyroid nodules within ultrasound images using super-resolution single-image reconstruction and deep learning technology. The results indicate that the proposed method is superior to the latest and best techniques in terms of accuracy and quality. This research contributes to the advancement of medical imaging and holds the potential to improve the diagnosis and treatment of thyroid nodules.

Previous studies have indicated the abnormality of the globus pallidus in neonates with hyperbilirubinemia.

This study aims to explore the microstructure and cerebral perfusion of globus pallidus in neonatal hyperbilirubinemia by using Diffusion Tensor Imaging (DTI) and Arterial Spin Labeling (ASL) approaches.

Thirty-seven neonates were enrolled in this study, which were classified into Bilirubin-Induced Neurologic Dysfunction (BIND) group (hyperbilirubinemia with BIND, n=12), non-BIND group (hyperbilirubinemia without BIND, n=15), and healthy controls (HC) group (n=10). The quantitative values of globus pallidus were calculated from DTI, including the Apparent Diffusion Coefficient (ADC), the Fractional Anisotropy (FA), and Volume Ratio (VR) values. Additionally, the relative Cerebral Blood Flow (rCBF) values were obtained from ASL.

It was observed that the mean DTI signal of globus pallidus was significantly different among the three groups (p < 0.05). However, there were no significant differences in the rCBF of globus pallidus among the three groups (p > 0.05). A positive correlation was also observed between the fractional anisotropy (FA) value and serum bilirubin level (r = 0.561, p = 0.002), while the VR value showed a negative correlation with serum bilirubin level (r=-0.484, p=0.011). The area under the curve (AUC) of FA, VR, and FA and VR combined was 0.897, 0.858, and 0.933, respectively.

The alterations of microstructure in globus pallidus, especially FA and VR value, may be valuable and sensitive at the early stage of hyperbilirubinemia encephalopathy, suggesting that early hyperbilirubinemia may lead to cytotoxic edema and decreased permeability of the cell membrane.

Columnar cell carcinoma is a rare subtype of papillary thyroid carcinoma (CCV-PTC) that accounts for only 0.15% to 0.2% of all Papillary Thyroid Carcinomas (PTCs). It has aggressive behavior but a better prognosis than anaplastic thyroid carcinoma.

A 64-year-old female presented with a huge thyroid mass resulting in compressive myelopathy and was diagnosed as CCV-PTC, not anaplastic carcinoma. After multidisciplinary discussions, we decided to proceed with otolaryngological, thoracic, and orthopaedic surgery. All tumours were unresectable, and we planned to proceed with R2 resection to resolve the gait disturbance and anterior fusion to resolve spinal instability.

Advanced-stage thyroid cancer is relatively uncommon, but desirable treatment effects can be expected through accurate pathological diagnosis. Immunohistochemical staining and tissue-specific markers can be helpful.

In developing Computer-Aided Diagnosis (CAD), a Convolutional Neural Network (CNN) has been commonly used as a Deep Learning (DL) model. Although it is still early, DL has excellent potential in implementing computers in medical diagnosis.

This study reviews the use of DL for Anterior Cruciate Ligament (ACL) tear diagnosis. A comprehensive search was performed in PubMed, Embase, and Web of Science databases from 2018 to 2024. The included study criteria used MRI images to evaluate ACL tears, and the diagnosis of ACL tears was performed using the DL model. We summarized the paper by reporting their model accuracy, model comparison with arthroscopy, and explainable.

AI implementation in tabular format; we conclude that many medical professionals believe that arthroscopic diagnosis is the most reliable method for diagnosing ACL tears. However, due to its intrusive treatment, CAD is projected to be able to produce similar outcomes from MRI scan results. To gain the trust of physicians and meet the demand for reliable knee injury detection systems, an algorithm for CAD should also meet several criteria, such as being transparent, interpretable, explainable, and easy to use. Therefore, future works should consider creating an Explainable DL model for ACL tear diagnosis. It is also essential to evaluate the performance of this Explainable DL model compared to the gold standard of arthroscopy diagnosis.

There are issues regarding the need for Explainable DL in CAD to increase confidence in its result while also highlighting the importance of the involvement of medical practitioners in system design. There is no funding for this work.

Our aim was to explore the feasibility of using radiomics data derived from intratumoral and peritumoral edema on fat-suppressed T2-weighted imaging (T2 FS) to distinguish triple-negative breast cancer (TNBC) from non-triple-negative breast cancer (non-TNBC).

This retrospective study enrolled 174 breast cancer patients. According to the MRI examination time, patients before 2021 were divided into training (n = 119) or internal test (n = 30) cohorts at a ratio of 8:2. Patients from 2022 were included in the external test cohort (n = 25). Four regions of interest for each lesion were defined: intratumoral regions, peritumoral edema regions, regions with a combination of intratumoral and peritumoral edema, and regions with a combination of intratumoral and 5-mm peritumoral. Four radiomic signatures were built using the least absolute shrinkage and selection operator (LASSO) method after selecting features. Furthermore, a radio mic-radiological model was constructed using a combination of intratumoral and peritumoral edema regions along with clinical-radiologic features. Area under the receiver operating characteristic curve (AUC) calculations, decision curve analysis, and calibration curve analysis were performed to assess the performance of each model.

The radiomic-radiological model showed the highest AUC values of 0.906 (0.788-1.000) and 0.825 (0.622-0.947) in both the internal and external test sets, respectively. The radiology-radiomic model exhibited excellent predictive performance, as evidenced by the calibration curves and decision curve analysis.

The ensemble model based on T2 FS-based radiomic features of intratumoral and peritumoral edema, along with radiological factors, performed better in distinguishing TNBC from non-TNBC than a single model. We explored the possibility of developing explainable models to support the clinical decision-making process.

Lumbar disc herniation (LDH) is a common clinical condition causing lower back and leg pain. Accurate segmentation of the lumbar discs is crucial for assessing and diagnosing LDH. Magnetic resonance imaging (MRI) can reveal the condition of articular cartilage. However, manual segmentation of MRI images is burdensome for physicians and needs to be more efficient.

In this study, we propose a method that combines UNet and superpixel segmentation to address the problem of loss of detailed information in the feature extraction phase, leading to poor segmentation results at object edges. The aim is to provide a reproducible solution for diagnosing patients with lumbar disc herniation.

We suggest using the network structure of UNet. Firstly, dense blocks are inserted into the UNet network, and training is performed using the Swish activation function. The Dense-UNet model extracts semantic features from the images and obtains rough semantic segmentation results. Then, an adaptive-scale superpixel segmentation algorithm is applied to segment the input images into superpixel images. Finally, high-level abstract semantic features are fused with the detailed information of the superpixels to obtain edge-optimized semantic segmentation results.

Evaluation of a private dataset of multifidus muscles in magnetic resonance images demonstrates that compared to other segmentation algorithms, this algorithm exhibits better semantic segmentation performance in detailed areas such as object edges. Compared to UNet, it achieves a 9.5% improvement in the Dice Similarity Coefficient (DSC) and an 11.3% improvement in the Jaccard Index (JAC).

The experimental results indicate that this algorithm improves segmentation performance while reducing computational complexity.

Residual breast tumors may remain after vacuum-assisted excisional biopsy (VAEB).

To determine the incidence of residual breast tumors in patients after VAEB and the efficacy of magnetic resonance imaging (MRI) in detecting these tumors.

This retrospective analysis examined patients who received VAEB before a diagnosis of breast cancer (BC) at our hospital from 2015 to 2019. The incidence of residual tumors after VAEB was determined by MRI and pathological examination. The diagnostic value of MRI in detecting residual tumors was determined for all patients and different subgroups. Logistic regression analysis was used to identify factors associated with residual tumors.

We examined 147 patients and obtained pathological samples from 146 patients, including 103 (70.5%) with residual tumors and 43 (29.5%) without residual tumors. The MRI examinations demonstrated the complete tumor resection rate was 48.9%. Compared to the pathological results, MRI had a positive predictive value of 77.8%, negative predictive value of 48.8%, specificity of 65.6%, and sensitivity of 60.7%. Further analysis indicated that MRI had moderate accuracies for patients with stage pT-1 (71.9%), stage pTNM-IA (73.1%), and luminal B subtype (78.3%). Binary logistic regression analysis showed that the risk of tumor residue correlated with the pathological stage.

Tumor residue is common after VAEB, and MRI has limited accuracy in detecting these residual tumors. However, for small breast tumors and luminal B subtype BC, MRI had higher accuracy in the detection of residual tumors. The risk of tumor residue is closely associated with the pathological stage.

The purpose of this study was to evaluate the diagnostic value of colour Doppler sonography for ovarian veins. The clinical incidence of ovarian venous lesions is relatively low and often overlooked. The ovarian veins are located deep in the pelvis, and they are relatively elongated, which could make medical imaging more difficult. Therefore, there is limited literature on the diagnosis of ovarian venous disease. The purpose of this study was to evaluate the diagnostic value of colour Doppler sonography towards ovarian vein.

A total of 37 consecutive patients with clinically suspected ovarian venous disorders were included. All the patients underwent colour Doppler sonography. CTV was performed in 31 patients, while retrograde phlebography was performed in 6 patients. CT/phlebography was the established diagnostic criterion for ovarian vein disorders. The SPSS 22.0 program was used for statistical analysis. Sensitivity, specificity, and positive and negative predictive values for colour Doppler sonography were calculated. k-test was used to evaluate consistency between colour Doppler sonography and CT/phlebography.

In the 37 patients,18 cases were positive for ovarian vein disorders and 19 cases were negative, as assessed with colour Doppler sonography. The associated lesions included ovarian vein thrombosis (7 cases), ovarian varicocele (3 cases), and ovarian venous leiomyoma (8 cases). The calculated values of sensitivity, specificity, and positive and negative predictive value were 94.4%, 94.7%, 94.4%, and 94.7%, respectively. The overall accuracy rate was 94.9%. The K level of the degree of agreement between CT/phlebography and colour Doppler sonography was 0.892.

Colour doppler sonography can provide sufficient imaging information. In clinical ultrasonography, attention should be paid to recognizing and detecting ovarian venous lesions.

A prosthetic device is designed based on the quantitative analysis of muscle MRI which will improve the muscle control achieved with functional electrical stimulation/ guided robotic exoskeletons. Electromyography (EMG) provides muscle functionality information while MRI provides the physiological and functionality of muscles. The sensor feedbacks were used for the bionic prosthesis, but the length of muscle using image processing was not correlated .

To investigate and perform qualitative and quantitative assessment of thigh muscle using MRI. The objective of the work is to improve the existing VAG signal classification method to diagnose abnormality using MRI for patients undergoing Total knee replacement (TKR).

A deep learning method for qualitative and quantitative assessment of thigh muscle is done using MRI. In existing prosthetic devices, electrical measurements of a person’s muscles are obtained using surface or implantable electrodes. Several methods were used for the classification and diagnostic processes. The existing methods have drawbacks in feature extraction and require experts to design the system. This work combines medical image processing and orthopaedic prosthetics to develop a therapeutic method.

This design provides much more precise control of prosthetic limbs using the image processing technique. The hybrid CNN swarm-based method measures the muscle structure and functions. Along with the sensor readings, these details are combined for prosthetic control. The implementation was carried out in MATLAB, Sketchuppro, and Arduino IDE.

A combined swarm intelligence and deep learning method were proposed for qualitative and quantitative assessment of thigh muscle. The prosthetic device choice was done from the scanned MRI image like Humerus-T prosthetics, segmental prosthesis and arthrodesis prosthesis. The investigation was done for the Total knee replacement (TKR) approach

With the in-depth development of assistive treatment devices, the application of artificial knee joints in the rehabilitation of amputees is becoming increasingly mature. The length of residual limbs and muscle strength of patients have individual differences, and the current artificial knee joint lacks certain adaptability in the personalized rehabilitation of patients.

In order to deeply analyze the impact of different types of artificial knee joints on the walking function of unilateral thigh amputees, improve the performance of artificial knee joints, and enhance the rehabilitation effect of patients, this article combines image processing technology to conduct in-depth research on the walking gait analysis of different artificial knee joints of unilateral thigh amputees.

This article divides patients into two groups: the experimental group consists of patients with single leg amputation, and the control group consists of patients with different prostheses. An image processing system is constructed using universal video and computer hardware, and relevant technologies are used to recognize and track landmarks; Furthermore, image processing technology was used to analyze the gait of different groups of patients. Finally, by analyzing the different psychological reactions of amputees, corresponding treatment plans were developed.

Different prostheses worn by amputees have brought varying degrees of convenience to life to a certain extent. The walking stability of wearing hydraulic single axis prosthetic joints is only 79%, and the gait elegance is relatively low. The walking stability of wearing intelligent artificial joints is as high as 96%. Elegant gait is basically in good condition.

Image processing technology helps doctors and rehabilitation practitioners better understand the gait characteristics and rehabilitation progress of patients wearing different artificial knee joints, providing objective basis for personalized rehabilitation of patients.

The recent advancements and detailed studies in the field of 3D bioprinting have made it a promising avenue in the field of organ shortage, where many patients die awaiting transplantation. The main challenges bioprinting faces are precision during printing, vascularization, and cell proliferation. Additionally, overcoming these shortcomings requires experts from engineering, medicine, physics, etc., and if accomplished, it will significantly benefit humankind.

This paper covers the general roadmap of the bioprinting process, different kinds of bioinks, and available bioprinters. The paper also includes designing the anatomical structure, which is the first phase of the bioprinting process, and how AI has facilitated this entire process of 3D printing in healthcare and associated applications like medical modelling and disease modelling.

The process of 3D bioprinting involves meticulous structure designing of the anatomical structure under study, which forms the base of the entire bioprinting process. One of the significant applications of 3D printing in healthcare is Medical Modelling and Disease Modelling, which requires the detection of disease in anatomy and its delineation from the rest of anatomy for meticulous creation of ROI using sophisticated segmentation software(s) for the construction of 3D models of diseased anatomy and healthy anatomical surroundings.

The study concluded that bioprinting is the future of the worldwide organ transplantation crisis. Anatomical accuracy is an important aspect that must be considered while producing 3D models. The reproduction of patient-specific 3D models requires human rights and ethics approval under four principles of ethics in healthcare: autonomy, non-maleficence, beneficence, and justice.

This study aims to investigate the association of preoperative body composition parameters, measured by computed tomography in patients undergoing surgery for renal cell carcinoma, with its stage and to survey the relationship with postoperative hospitalization duration and survival.

Demographic data, pathology results, cancer stages, and hospitalization duration of 104 patients undergoing surgery at the urology clinic due to renal cell carcinoma between 2019 and 2023 were analyzed retrospectively. On computed tomography scans acquired during diagnosis, visceral adipose tissue, subcutaneous adipose tissue, total adipose tissue, and skeletal muscle area were measured. The ratios of body composition parameters were computed.

When the correlation between survival time and body composition in deceased patients was analysed, a moderate but significant correlation was observed between skeletal muscle area value and total adipose tissue / skeletal muscle area ratio (r=0.630, p=0.001; r=0.598, p=0.002). A significant and strong correlation was observed between total adipose tissue value and survival (r=0.704, p<0.001). Subcutaneous adipose tissue / skeletal muscle area was found to be an independent risk factor associated with mortality, and a ratio of 0.98 or less increased the mortality risk approximately 16-fold.

The relationship between body composition parameters measured by computed tomography, which can be easily evaluated pre-treatment, and mortality, postoperative recovery and length of hospital stay can be evaluated, giving clinicians an idea about the potential difficulties that patients may encounter during the treatment process. For this purpose, the subcutaneous adipose tissue / skeletal muscle area ratio is the most helpful parameter that can be used.

Glutamine Synthetase (GS) could induce vascular sprouting through the improvement of endothelial cell migration in inflammatory diseases. MR vessel-size imaging has been proposed as a valuable approach for visualizing the underlying angiogenic processes in the brain.

This study aims to investigate the role of GS in the neovascularization of gliomas through the utilization of MR vessel-size imaging and histopathological techniques.

In this exploratory animal study, we randomly divided the C6 glioma rat models into a control group and an L-methionine sulfoximine (MSO) treatment group. Daily intraperitoneal injections were administered for three consecutive days, starting from day 10 following the implantation of C6 glioma cells in rats. Subsequently, MR vessel size imaging was conducted using a BRUKER 7 T/200 mm MRI scanner, and the MRI results were validated through histopathological examination.

A significant decrease in microvessel density was observed in both the tumor periphery and center areas in the MSO treatment group compared to that in the control group. The mean vessel diameter (mVD) and vessel size index (VSI) did not exhibit significant changes compared to the control group. Moreover, the staining intensity of platelet endothelial cell adhesion molecule-1 (CD31) and GS in the tumor periphery was significantly decreased in the MSO treatment group. Additionally, the MSO treatment demonstrated a substantial inhibition of tumor growth.

GS inhibitors significantly reduced angiogenesis in the periphery area of C6 glioma, exerting an inhibitory effect on tumor progression. Thus, GS inhibitors could be potential therapeutic agents for treating glioma. Additionally, in vivo MR vessel size imaging detects changes in vascular-related parameters after tumor treatment, making it a promising method for detecting neovascularization in glioma.

Diffusion Magnetic Resonance Imaging (MRI) is a useful method to evaluate tumor biology and tumor microstructure. The apparent diffusion coefficient (ADC) value correlates negatively with the cellular density of the tumor.

This study aimed to investigate the effectiveness of the ADC histogram analysis in showing the relationship between breast cancer prognostic factors and ADC parameters.

This study is a retrospective observational descriptive study. ADC histogram parameters were evaluated in all tumor volumes of 67 breast cancer patients. Minimum, 5, 10, 25, 50, 75, 90, 95 percentiles, maximum, mean, median ADC values, kurtosis, and skewness were calculated. Breast MRI examinations were performed on a 3T MR scanner. We evaluated the fibroglandular tissue density of bilateral breasts, background enhancement, localization of masses, multifocality-multicentricity, shape, rim, internal contrast enhancement, and kinetic curve on breast MRI. BI-RADS scoring was performed according to breast MRI. Pathologically, histologic type, histologic grade, HER 2, Ki 67, ER-, and PR status were evaluated.

A significant correlation was found between tumor volume and ADC scores. There is a significant correlation between min ADC values (p< 0.031), max ADC (p< 0.001), and skewness (p< 0.019). A significant correlation was found between tumor kurtosis and lymph nodes (p< 0.029). There was a significant difference in ADC mean, ADC10%, ADC25%, ADC50%, ADC75%, ADC90%, ADC 95% and ADCmax values depending on ER-and PR-status. (for ER p = 0.004, p = 0.018, p = 0.010, p = 0.008, p = 0.004, p = 0.004, p = 0.02, p = 0.02 and p = 0.038, for PR p < 0.001, p = 0.028, p = 0.011, p = 0.001, p < 0.001, p =<0.001, p < 0.001, and p < 0.001, respectively; p < 0.05). These values were lower in ER-and PR-positive status than in ER-and PR-negative receptor status. According to HER2 status, there was a statistically significant difference in ADC5% and measurements of the lesions (p = 0.041; p < 0.05). Our study found no significant correlation between other prognostic factors, such as histological grade, Ki-67 indices, and ADC values.

Our study found a significant difference between tumor volume, ER- and, PR status, HER2, and lymph node involvement, and some ADC values among prognostic factors for breast cancer. Furthermore, ADC histogram analysis can provide additional value in predicting some prognostic factors.

Alterations in ocular blood flow play an important role in the pathogenesis of diabetic macular edema; however, this remains unclear.

This study aimed to investigate ocular blood flow in eyes with or without diabetic macular edema using arterial spin labeling.

This cross-sectional study included 118 eyes of 65 patients with diabetic retinopathy analyzed between November 2018 and December 2019. We included a total of 53 eyes without diabetic macular edema (mean [SD] age, 57.83 [7.23] years; 29 men [54.7%]) and 65 eyes with diabetic macular edema (mean [SD] age, 60.11 [7.63] years; 38 men [58.5%]). Using a 3.0-T magnetic resonance imaging, participants were imaged with arterial spin labeling with multiple post-labeling delays.

The mean ocular blood flow at post-labeling delays of 1.5 and 2.5 s was significantly lower in eyes with diabetic macular edema among patients with diabetic retinopathy compared with the remaining subgroups (P=0.022 and P <0.001, respectively). The mean ocular blood flow exhibited a significant decrease in eyes with diabetic macular edema when the post-labeling delay was set at 2.5 s in the nonproliferative and proliferative diabetic retinopathy groups, compared with the remaining subgroups (P=0.005 and P=0.002, respectively). The cutoff points of ocular blood flow at post-labeling delays of 1.5 s and 2.5 s were 9.40 and 11.10 mL/100 g/min, respectively.

Three-dimensional pseudocontinuous arterial spin labeling can identify differences in the ocular blood flow of patients with diabetic eyes with and without diabetic macular edema.

Ovarian hemangioma is an extremely rare tumor with atypical clinical manifestations, often discovered incidentally during autopsy or surgery. Approximately 60 cases have been reported in the past, but no more than 10 cases have been investigated by MRI and ultrasound (US).

ln this paper, we reported a 51-year-old female patient with Ovarian Hemangioma who had no symptoms of abdominal pain, abnormal vaginal bleeding or discharge, or any other discomfort. Laboratory tests revealed an elevated serum carbohydrate antigen (CA125) of 48.99U/ml (reference range: 0-35U/ml). Multiparametric 3.0T magnetic resonance imaging (MRI) showed a cystic solid mass with a clear boundary and regular shape in the left ovarian area and minimal ascites in the abdominal cavity. The histological examination of the mass confirmed an ovarian hemangioma.

The MRI findings of ovarian hemangiomas are highly similar to those observed in hepatic hemangiomas, emphasizing the distinctive radiological characteristics specific to this condition in the ovary. This paper presents an overview of the typical MRI findings associated with ovarian hemangioma, which holds great importance for accurate diagnosis and effective treatment.

Accurate finding of Knee Osteoarthritis (KOA) from structural Magnetic Resonance Imaging (MRI) is a difficult task and is greatly subject to user variation. Furthermore, the identification of knee osteoarthritis (KOA) from MRI scans presents a challenge due to the limited information available. A novel methodology using an ensemble Deep Learning algorithm, combining EfficientNet-B3 and ResNext-101 architectures, aims to forecast KOA advancement, bridging the identified gap in clinical trials.

The study aims to develop a precise predictive model for knee osteoarthritis using advanced deep-learning architectures and structural MRI scan data. By utilizing an ensemble technique, the model's accuracy in predicting disease development is enhanced, surpassing the limitations of traditional biomarkers.

The study used the Osteoarthritis Initiative dataset to develop an ensemble Deep Learning model that combined EfficientNet-B3 and ResNext-101 architectures. Techniques like cropping, gamma correction, and in-slice rotation were used to expand the dataset and improve the model's generalization capacity.

The Deep Learning model demonstrated 93% validation accuracy on the OAI dataset, accurately capturing subtle patterns of knee osteoarthritis progression. Augmentation approaches enhanced its resilience.

Our ensemble Deep Learning approach, using ResNext-101 and EfficientNet-B3 architectures, accurately predicts knee osteoarthritis courses using structural MRI data, demonstrating the importance of data augmentation for improved predictive tools.

This study aims to observe the fluctuating urine iodine levels in patients with differentiated thyroid cancer (DTC) following iodinated contrast-enhanced computed tomography (eCT) scans.

The presence of iodine in iodinated contrast agents (ICAs) can impede the effectiveness of radioactive iodine treatment (RAIT) and diagnostic scans in individuals diagnosed with DTC, as it can engage in competitive interactions with ¹³¹I. According to established guidelines, it is recommended to postpone RAIT for a period of three to four months in individuals who have had prior exposure to ICAS. The measurement of spot urine iodine concentration is a valuable indicator for assessing the overall iodine content throughout the body.

The objective is to identify the optimal timing for administering postoperative RAIT in DTC patients.

At various time points after surgery, a cohort of 467 random urine samples (126 male samples, 341 female samples, age (45±12 years)) was obtained from 269 DTC patients. The samples were analyzed for urinary iodine and urinary creatinine levels, and the urinary iodine/urine creatinine ratio (I/Cr) was computed. All samples were divided into two groups according to whether eCT before operation: the non-enhanced CT (eCT-) group and the enhanced CT (eCT+) group. The urine samples in the eCT- group were categorized into four subgroups according to the duration of strict low iodine diet (LID): (eCT-I+) no LID; (eCT-I-2W) 2 weeks of LID; (eCT-I-4W) 4 weeks of LID; and (eCT-I-6W) 6 weeks of LID. The last three groups were merged into the eCT- and effective LID group (eCT- I-). The urine samples from the eCT+ group were categorized into five subgroups: (0.5M eCT+)0.5 month after eCT+; (1M eCT+)1 month after eCT+; (2M eCT+) 2 months after eCT+; (3M eCT+) 3 months after eCT+; (≥4M eCT+) ≥4 months after eCT+. In addition, the patients within 2 months after eCT+ were divided into 2 groups according to their LID: no effective LID group (eCT+ I+) and effective LID group (eCT+ I-). Utilizing the Kruskal-Wallis and Mann-Whitney U rank sum tests, the differences in I/Cr between groups were compared.

In the eCT-group, the I/Cr ratios of eCT-I-2W, eCT-I-4W, and eCT-I-6W were significantly lower than those of eCT-I+ (χ2 values: 4.607.99, all P 0.05). However, there was no significant difference in I/Cr between eCT-I-2W, eCT- I-4W, and eCT-I-6W (2 values: 0.591.31, all P > 0.05). Significantly higher I/Cr values were observed in 0.5M eCT+ and 1M eCT+ than in eCT-I+ (χ2 values: 3.22 and 2.18, respectively, all P<0.05). There was no significant difference in I/Cr between 2M eCT+ and eCT-I+ (χ2 = 0.76, P = 0.447). The I/Cr rations of 3M eCT+, ≥4M eCT+ were not significantly different with eCT-I- (χ2 values: 1.76; 0.58; all P > 0.05). However, they were considerably lower than eCT-I+ (χ2 values: 7.03; 5.22; all P<0.05). The I/Cr for patients who underwent eCT within two months (eCT+ I-, eCT+ I+) did not differ significantly (χ2 = 1.79, P = 0.073).

For patients who are considering receiving radioactive iodine therapy (RAIT) following a diagnosis of differentiated thyroid cancer (DTC), it is recommended that the interval between RAIT treatment and enhanced computed tomography [eCT] scans be conducted at least three months.

The study aimed to evaluate whether the measurement of Femoral Neck Shaft Angle (FNSA) can be helpful in differentiating femoral head Stress Fracture (SF) from Avascular Necrosis (AVN).

From September 2019 to April 2022, sixty-four patients [median age 32.0 years, interquartile range (IQR) 23.0–39.0 years] who underwent both hip radiograph and Magnetic Resonance Imaging (MRI) and diagnosed as femoral head SF or AVN were included in our retrospective study. Patients were divided into as having either femoral head SF (n = 34) or AVN (n = 30). The FNSA was measured in anteroposterior hip radiography. Continuous values were compared using the Mann-Whitney U test. The assessment of the predictive value of FNSA for femoral head SF was performed by Receiver Operating Characteristic (ROC) analysis.

The FNSA was significantly higher in patients with SF (median 133.5°, IQR 128.0–136.7°) than those with AVN (median 127.5°, IQR 124.0–132.0°) (p = 0.001). In addition, the FNSA was significantly higher in SF femurs (median 134.8°, IQR 129.2–137.4°) than in contralateral normal femurs (median 127.1°, IQR 124.3–132.5°) in patients with unilateral femoral head SF (n = 30) (p < 0.001). In ROC analysis, the sensitivity, specificity, and Area Under the Curve (AUC) for predicting the femoral head SF were 77.3%, 63.3%, and 0.785 (95% confidence interval: 0.666–0.905), respectively, at a cutoff of 130.2°.

Increased FNSA was associated with femoral head SF; thus, measurement of FNSA could be helpful for differentiating femoral head SF from AVN.

Chronic Kidney Disease (CKD) affects individuals of different age groups worldwide. Moreover, CKD is associated with several risk factors, including obesity, lifestyle, and hypertension, which are common in the Middle East. Ultrasonography is the examination of choice for CKD. In recent years, Shear Wave Elastography (SWE) has developed through the continued development of ultrasound and received substantial attention ;therefore, it can be used to measure tissue stiffness. The study aimed to use point Shear Wave Elastography (p-SWE) to determine the correlation between diabetes and cortical renal thickness in detecting pathologies.

This study was performed at the King Abdul-Aziz University Hospital. We examined 61 patients who underwent SWE. The patients were classified into two groups based on the presence or absence of type 2 Diabetes Mellitus (DM).

The results showed that there was a significant correlation between cortical stiffness and DM duration [p<0.005]. In addition, there was a negative correlation between cortical stiffness and cortical thickness [p=0.147] in patients with DM. Moreover, the eGFR decreased with an increase in cortical stiffness [p=0.499]. The cortical thickness in patients with and without DM was 0.750 ± 0.2 kPa and 0.788 ± 0.4 kPa, respectively. The kidney stiffness in patients with DM and control patients was 8.5 ± 8.6 cm and 14.0 ± 25.16 cm, respectively.

This study showed that kidney p-SWE measurements were reliable. Therefore, further studies assessing kidney stiffness in patients with and without people with diabetes are recommended.

HAA is a significant risk factor in complex CoA patients. We conducted a retrospective study to explore the relationship between HAA and other cardiovascular factors.

We analyzed 103 patients diagnosed with complex CoA using CT angiography and echocardiography. Aortic diameter was measured at six levels, and severe coarctation was defined as coarctation site to diaphragmatic level ratio (CDR) < 50%. Correlations between non-HAA and HAA groups were assessed. Univariate and multivariate logistic regression identified HAA risk factors.

Among 103 children with complex CoA, 55 were in the non-HAA group and 48 in the HAA group. The incidence of PDA (56.3% vs. 32.7%, p < 0.05), severe coarctation (CDR < 50%, 81.3% vs. 34.5%, p < 0.01), and collateral arteries (39.6% vs. 0, p < 0.01) were higher in the HAA group than one in the non-HAA group. The aortic arch size was positively correlated with age and negatively correlated with severe coarctation, VSD, collateral arteries, and left heart dysfunction. Logistic regression results showed that collateral arteries were risk factors for the whole aortic arch (proximal arch OR = 11.458; p < 0.01, distal arch OR = 4.211; p < 0.05, and isthmus OR = 11.744; p < 0.01), severe coarctation (OR = 6.653; p < 0.01), and left heart dysfunction (OR = 5.149; p < 0.01) associated with isthmus hypoplasia.

This study highlights the prevalence of HAA in complex CoA patients and its associations with various cardiovascular factors. These insights improve diagnosis and treatment approaches.

Intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) is considered a potential marker of hepatic fibrosis (HF).

To explore the influencing factors of repeatability and reliability in IVIM-DWI parameters of ROI-based liver segments in participants with HF and healthy volunteers (HV) and to assess the diagnostic efficiency of these parameters in HF.

Participants with early HF (EHF, n=59) or advanced HF (AHF, n=38) and HV (n=48) were recruited. Two examiners measured IVIM data using mono-, bi-exponential and stretched exponential models. The results and influencing factors of repeatability and reliability of IVIM-DWI, and the diagnostic efficiency were analyzed.

The repeatability of D* (CV: 26.62–41.47%) and DDC (CV: 18.01–34.40%) was poor, the repeatability of ADC (CV: 4.95–9.76%), D (CV: 7.09–15.52%), f (CV: 9.35–17.15%), and α (CV: 7.48–13.81%) was better; ordered logistic regression showed statistically significant results of IVIM-derived parameters; the reliability showed no obvious trend, and ordered logistic regression showed statistically significant results of IVIM-derived parameters, groups, and partial hepatic segments (all p<0.001). IVIM-derived parameters with relatively good repeatability (CV<20%) and reliability (ICC>0.4) were used to establish regression models for differential diagnosis. The AUC of regression models was 0.744–0.783 (EHF vs. AHF), but no statistically significant parameters were found in the HV vs EHF comparison.

IVIM-derived parameters were the most important factors affecting the repeatability and reliability, while staging of HF and hepatic segments may be the influencing factors of reliability. IVIM-derived parameters showed medium diagnostic efficiency in distinguishing between EHF and AHF.

Registered on Clinical Trial Management Public Platform (registration code: ChiCTR2100052114, date: 17^th Oct. 2021).

Brain tumor is a grave illness causing worldwide fatalities. The current detection methods for brain tumors are manual, invasive, and rely on histopathological analysis. Determining the type of brain tumor after its detection relies on biopsy measures and involves human subjectivity. The use of automated CAD techniques for brain tumor detection and classification can overcome these drawbacks.

The paper aims to create two deep learning-based CAD frameworks for automatic detection and severity grading of brain tumors – the first model for brain tumor detection in brain MR images and model 2 for the classification of tumors into three types: Glioma, Meningioma, and Pituitary based on severity grading.

The novelty of the research work includes the architectural design of deep learning frameworks for detection and classification of brain tumor using brain MR images. The hyperparameter tuning of the proposed models is done to achieve the optimal parameters that result in maximizing the models' performance and minimizing losses.

The proposed CNN models outperform the existing state of the art models in terms of accuracy and complexity of the models. The proposed model developed for detection of brain tumors achieved an accuracy of 98.56% and CNN Model developed for severity grading of brain tumor achieved an accuracy of 92.36% on BraTs dataset.

The proposed models have an edge over the existing CNN models in terms of less complexity of the structure and appreciable accuracy with low training and test errors. The proposed CNN Models can be employed for clinical diagnostic purposes to aid the medical fraternity in validating their initial screening for brain tumor detection and its multi-classification.

Embryonal carcinoma is a rare tissue type in germ cell tumors. According to our literature review, metastatic embryonal carcinoma misdiagnosed as lymphoma because of its high similarity to lymphoma is extremely rare and has not been reported yet.

A 46-year-old middle adulthood male presented with unexplained fever, night sweats, abdominal distension for 3 months, and weight loss of around 7kg during almost 6 months, which is extremely similar to lymphoma from the clinical features and imaging examinations. After a clear diagnosis, the case not only obtained the opportunity of surgery but was also exempted from radiotherapy. The treatment effect was good. We report a case of rare metastatic embryonal carcinoma, which can provide insight into the diagnosis and treatment of embryonal carcinoma.

Metastatic embryonal carcinoma of abdominal lymph nodes can be highly similar to lymphoma; the diagnosis can only be based on clinical manifestations and imaging examination but also combined with patient history, tumor markers and biochemical examination. However, the final diagnosis depends on pathological biopsy.

To investigate the feasibility of image characteristics and radiomics combined with machine learning based on Gd-EOB-DTPA-enhanced MRI for functional liver reserve assessment in cirrhotic patients.

123 patients with cirrhosis were retrospectively analyzed; all our patients underwent pre-contrast MRI, triphasic (arterial phase, venous phase, equilibrium phase) Gd-EOB-DTPA dynamic enhancement and hepatobiliary phase (20 minutes delayed). The relative enhancement (RE) of the patient's liver, the liver-spleen signal ratio in the hepatobiliary phase (SI liver/ spleen), the liver-vertical muscle signal ratio in the hepatobiliary phase (SI liver/ muscle), the bile duct signal intensity contrast ratio (SIR), and the radiomics features were evaluated. The support vector machine (SVM) was used as the core of machine learning to construct the liver function classification model using image and radiomics characteristics, respectively.

The area under the curve was the largest in SIR to identify Child-Pugh group A versus Child-Pugh group B+C in the image characteristics, AUC = 0.740, and Perc. 10% to identify Child-Pugh group A versus Child-Pugh group B+C in the radiomics characteristics, AUC = 0.9337. The efficacy of the SVM model constructed using radiomics characteristics was better, with an area under the curve of 0.918, a sensitivity of 95.45%, a specificity of 80.00%, and an accuracy of 89.19%.

The image and radiomics characteristics based on Gd-EOB-DTPA-enhanced MRI can reflect liver function, and the model constructed based on radiomics characteristics combined with machine learning methods can better assess functional liver reserve.

This study evaluates the effectiveness of artificial intelligence (AI) in mammography in a diverse population from a middle-income nation and compares it to traditional methods.

A retrospective study was conducted on 543 mammograms of 467 Malays, 48 Chinese, and 28 Indians in a middle-income nation. Three breast radiologists interpreted the examinations independently in two reading sessions (with and without AI support). Breast density and BI-RADS categories were assessed, comparing the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) results.

Of 543 mammograms, 69.2% had lesions detected. Biopsies were performed on 25%(n=136), with 66(48.5%) benign and 70(51.5%) malignant. Substantial agreement in density assessment between the radiologist and AI software (κ =0.606, p < 0.001) and the BI-RADS category with and without AI (κ =0.74, p < 0.001). The performance of the AI software was comparable to the traditional methods. The sensitivity, specificity, PPV, and NPV or radiologists alone, radiologist + AI, and AI alone were 81.9%,90.4%,56.0%, and 97.1%; 81.0%, 93.1%,55.5%, and 97.0%; and 90.0%,76.5%,36.2%, and 98.1%, respectively. AI software enhances the accuracy of lesion diagnosis and reduces unnecessary biopsies, particularly for BI-RADS 4 lesions. The AI software results for synthetic were almost similar to the original 2D mammography, with AUC of 0.925 and 0.871, respectively.

AI software may assist in the accurate diagnosis of breast lesions, enhancing the efficiency of breast lesion diagnosis in a mixed population of opportunistic screening and diagnostic patients.

• The use of artificial intelligence (AI) in mammography for population-based breast cancer screening has been validated in high-income nations, with reported improved diagnostic performance. Our study evaluated the usage of an AI tool in an opportunistic screening setting in a multi-ethnic and middle-income nation.

• The application of AI in mammography enhances diagnostic accuracy, potentially leading to reduced unnecessary biopsies.

• AI integration into the workflow did not disrupt the performance of trained breast radiologists, as there is a substantial inter-reader agreement for BI-RADS category assessment and breast density.

Imaging techniques such as X-rays and 3D Computed Tomography (CT) are used to diagnose and evaluate a patient's shoulder before and after surgery. Identifying the kind, location, and severity of a shoulder fracture helps surgeons choose the right treatment and surgery.

The study examines the effectiveness of small incision reduction and superior closure pinning in treating Ideberg type III glenoid fractures identified by X-ray and CT scans.

From October 2017 to June 2022, 40 patients with Ideberg type III glenoid fractures underwent mini-incision reduction and superior closed pinning fixation using the Anterior (AA) and Posterior (PA) approaches. Pre- and post-surgery shoulder scores and imaging data were analyzed. Outpatient review and shoulder anteroposterior radiographs were collected at 1, 3, 6, and 12 months after surgery. We assessed shoulder joint function using the American Shoulder and Elbow Society (ASES) shoulder score, VAS score, Constant-Murley Shoulder Outcome (Constant) score, and DASH score.

A total of 40 patients were monitored for 14-16 months, averaging 15.2 ± 0.3 months. All fractures were healed between 14-25 weeks from X-rays, averaging 17.6 ± 5.4 weeks. Both the AA and PA groups had similar shoulder score changes. However, the AA group did better. In all cases, ASES shoulder scores were outstanding at 80%. Radiographs demonstratedno traumatic arthritis or internal fixation failure consequences like screw loosening or breakage.

It was concluded that Ideberg type III glenoid fracture reduction with an anterior small incision and superior closed pinning hollow lag screw internal fixation could be successful.

A recently developed deep-learning-based automatic evaluation model provides reliable and efficient Cobb angle measurements for scoliosis diagnosis. However, few studies have explored its clinical application, and external validation is lacking. Therefore, this study aimed to explore the value of automated assessment models in clinical practice by comparing deep-learning models with manual measurement methods.

The 481 spine radiographs from an open-source dataset were divided into training and validation sets, and 119 spine radiographs from a private dataset were used as the test set. The mean Cobb angle values assessed by three physicians in the hospital's PACS system served as the reference standard. The results of Seg4Reg, VFLDN, and manual measurement were statistically analyzed. The intra-class correlation coefficients (ICC) and the Pearson correlation coefficient (PCC) were used to compare their reliability and correlation. The Bland-Altman method was used to compare their agreement. The Kappa statistic was used to compare the consistency of Cobb angles at different severity levels.

The mean Cobb angle values measured were 35.89° ± 9.33° with Seg4Reg, 31.54° ± 9.78° with VFLDN, and 32.23° ± 9.28° with manual measurement. The ICCs for the reliability of Seg4Reg and VFLDN were 0.809 and 0.974, respectively. The PCC and MAD between Seg4Reg and manual measurements were 0.731 (p<0.001) and 6.51°, while those between VFLDN and manual measurements were 0.952 (p<0.001) and 2.36°. The Kappa statistic indicated VFLDN (k= 0.686, p< 0.001) was superior to Seg4Reg and manual measurements for Cobb angle severity classification.

The deep-learning-based automatic scoliosis Cobb angle assessment model is feasible in clinical practice. Specifically, the keypoint-based VFLDN is more valuable in actual clinical work with higher accuracy, transparency, and interpretability.

⁶⁸Ga-labeled prostate-specific membrane antigen positron emission tomography-computed tomography (⁶⁸Ga-PSMA PET/CT) has led to altered treatment plans for prostate cancer (PCa) patients.

This study aimed to investigate the impact of ⁶⁸Ga-PSMA PET/CT on overall survival (OS) and management in PCa.

Consecutive 100 patients who had ⁶⁸Ga-PSMA PET/CT and conventional imaging (CI) were included in this retrospective study. Disease stages and treatment plans according to both CI and ⁶⁸Ga-PSMA PET/CT were compared. The effect of ⁶⁸Ga-PSMA PET/CT on OS was assessed.

After ⁶⁸Ga-PSMA PET/CT, the stage changed in 64 patients (64%). By the reason of ⁶⁸Ga-PSMA PET/CT findings, treatment plans based on CI were changed in 73 patients (73%). According to the ROC analysis, patients with a PSA value below 8 had higher rates of change in staging (p<0.0001) and treatment (p=0.034). Both a PSA below 8 (OR 8.79 95% CI (2.72-28.43), p<0.001), and having a hormone-sensitive disease at the time of imaging (OR 5.6 95% CI (1.35-23.08), p=0.017) were significant independent factors predicting change in staging with ⁶⁸Ga-PSMA PET/CT. The results of a phi correlation coefficient analysis showed a significant relationship between therapy and changes in staging (ϕ=0.638, p<0.0001). Two-year OS was statistically different in hormone-sensitive patients with and without treatment change (95% vs 81%, p=0.006).

⁶⁸Ga-PSMA PET/CT has the effect of changing the treatment in 73% of PCa patients. There is a positive correlation between the changes in staging and treatment. Survival of hormone sensitive patients has improved due to treatment changes based on PET/CT findings.

Medical imaging mechanization has reformed medical management, empowering doctors to recognize cancer prematurely and promote patient outcomes. Imaging tests are of significant influence in the detection and supervision of cancer patients. Cancer recognition generally necessitates imaging studies that, in most instances, utilize a trivial amount of radiation. Methodologies such as X-rays, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) are predominant in clinical managerial, incorporating remedy and research.

Over recent years, diagnostic imaging has progressed from a state of commencement to an advanced level. Numerous modern imaging procedures have evolved. Although contemporary medical imaging comprises image exhibition together with image refining, computer-aided diagnosis (CAD), image inscribing and conserving, and image transference, the majority of which are embraced in picture documentation and communication processes.

This review targets to encapsulate toxicology information on skin cancer unpredictability essential to interpretation measures, report important factor that helps in defining skin cancer condition, and possible medical care alternatives or medical attention endorsed referring to diverse aspects involving the size and site of malignancy, the complications, patient’s priority and well being. We concisely review various therapy alternatives, methods of radiation autoimmunity, prime observational study designs of medical and distinct radiation resources and cancer risks, and current analysis methodologies and research precision.

The detail of this paper covers a brief review of research and evolution in medical imaging discipline and mechanism. This review considers the physiology of melanocytes and the pathogenesis of skin cancer using medical imaging. Also, a description of risk factors, prevention methods, screening, various diagnosis methods and different stages of skin cancer, sub-types and different types of treatment methods is provided in this paper for research and development.

To identify age-matched healthy volunteers, non-cirrhotic chronic liver disease (CLD) and cirrhotic patients based on portal hemodynamic parameters using 4D flow MRI.

A total of 10 age-matched healthy volunteers and 69 CLD patients were enrolled and underwent 4D flow MRI prospectively. 4D flow MR images were processed by an MD in biomedical engineering working on the GTFlow platform. Portal hemodynamic parameters include net flow (mL/cycle), flow volume per second through the lumen (mL/sec), average flow velocity (cm/sec), and maximum flow velocity (cm/sec). The difference in portal hemodynamic parameters of 4D flow MRI was compared among healthy volunteers, non-cirrhotic CLD patients and patients with cirrhosis by one-way ANOVA or Kruskal-Wallis nonparametric test and post hoc tests.

10 CLD patients without cirrhosis and 56 patients with cirrhosis were eventually included, along with 10 healthy volunteers who were divided into three groups. 3 patients with cirrhosis whose image quality did not meet the requirements were excluded. There were no significant differences in portal hemodynamic parameters among the three groups except portal average velocity (P > 0.05). There was no statistical difference in all portal hemodynamic parameters of 4D flow MRI between healthy volunteers and patients with cirrhosis (P > 0.05). There were significant differences in portal average velocity between non-cirrhotic CLD patients, healthy volunteers and patients with cirrhosis, respectively (11.44±3.93 vs 8.10±2.66, P=0.013; 11.44±3.93 vs 8.60±2.22, P=0.007).

Portal average velocity obtained by 4D flow MRI can be an auxiliary means to identify cirrhosis in patients with CLD.

Cardiac chamber dimensions and left ventricle (LV) wall thickness change with the cardiac cycle, in which researchers have set different time points for systole and diastole.

This study aimed to provide characteristics of normal heart and choose the correct cardiac cycle to measure maximum cardiac parameters for cardiovascular disease.

The parameters of left atrium (LA), LV, right atrium (RA), and right ventricle (RV), as well as the wall thickness of LV, were measured in different cardiac phases using cardiac computed tomography (CT). Then, their differences in different phases and the correlation between these parameters and traditional risk factors were analyzed. In addition, receiver operator characteristic curve (ROC) analyses was performed to estimate LA enlargement.

The dimensions of LA and RA as well as the wall thickness of LV reached the maximum at the phase of 35% – 45%, while the dimensions of LV and RV reached the maximum at 95% – 5%. However, the changes of LA-B (antero-posterior diameter), LV-D1 (basal dimension), RA-B (minor dimension), and RV-D2 (mid cavity dimension) were relatively more stable than other diameters during the cardiac cycle. The maximum LA-B diameter, LV-D1 diameter, RA-B diameter, and RV-D2 diameter as well as the maximum interventricular septum thickness were acquired. Heart rate (HR) and smoking were potential indicators of LV-D2 (mid cavity dimension), while HR and LV myocardial mass were potential indicators of LV-D3 (apical-basal dimension). In phase 45%, the cut-off value of LA-B was 37.12 mm, with high sensitivity for predicting LA enlargement.

Choosing the adaptive cardiac phase for evaluating cardiac chamber dimensions and wall thickness obtained by cardiac CT could provide a more accurate clinical measurement of the heart.

Congenital enterocolic fistula, an abnormal connection between the small intestine and the colon, is a rare condition with the potential for significant complications affecting the patient’s quality of life.

A 2 year and 7 months old girl presented with abdominal pain and diarrhea lasting more than 10 days. The formation of the intestinal fistula was first detected by ultrasound, and the blood flow in the intestinal wall was preliminally analyzed. Surgical exploration revealed a colonic fistula formed by the attachment of the jejunum to the descending colon. Postoperatively, symptoms improved; no secondary infection occurred and the fistula healed well.

Congenital colon fistula is rarely reported, and ultrasound is becoming more and more important in its diagnosis. Here, we report a case of congenital colonic fistula diagnosed by ultrasound. Ultrasound can dynamically and in real-time observe the intestinal condition, which is conducive to the early diagnosis and staging of congenital intestinal diseases and the determination of diagnosis and treatment schemes.

Coronary Heart Disease (CHD) is one of the most common types of cardiovascular disease, and Heart Failure (HF) is an important factor in its progression. We aimed to evaluate the diagnostic value and predictors of multiparametric Cardiac Magnetic Resonance (CMR) in CHD patients with HF.

The study retrospectively included 145 CHD patients who were classified into CHD (HF+) (n = 91) and CHD (HF–) (n = 54) groups according to whether HF occurred. CMR assessed LV function, myocardial strain and T1 mapping. Multivariate linear regression analyses were performed to identify predictors of LV dysfunction, myocardial fibrosis, and LV remodeling.

CHD (HF+) group had impaired strain, with increased native T1, ECV, and LVM index. The impaired strain was associated with LVM index (p < 0.05), where native T1 and ECV were affected by log-transformed amino-terminal pro-B-type natriuretic peptide (NT-proBNP) levels. ROC analysis showed the combination of global circumferential strain (GCS), native T1, and LVM had a higher diagnostic value for the occurrence of HF in CHD patients.

Meanwhile, log-transformed NT-proBNP was an independent determinant of impaired strain, increased LVM index, native T1 and ECV.

HF has harmful effects on LV systolic function in patients with CHD. In CHD (HF+) group, LV dysfunction is strongly correlated with the degree of LV remodeling and myocardial fibrosis. The combination of the three is more valuable in diagnosing HF than conventional indicators.

The Glypican 3 (GPC3)-positive expression in Hepatocellular Carcinoma (HCC) is associated with a worse prognosis. Moreover, GPC3 has emerged as an immunotherapeutic target in advanced unresectable HCC systemic therapy. It is significant to diagnose GPC3-positive HCCs before therapy. Regarding imaging diagnosis of HCC, dynamic contrast-enhanced CT is more common than MRI in many regions.

The aim of this study was to construct and validate a radiomics model based on contrast-enhanced CT to predict the GPC3 expression in HCC.

This retrospective study included 141 (training cohort: n = 100; validation cohort: n = 41) pathologically confirmed HCC patients. Radiomics features were extracted from the Artery Phase (AP) images of contrast-enhanced CT. Logistic regression with the Least Absolute Shrinkage and Selection Operator (LASSO) regularization was used to select features to construct radiomics score (Rad-score). A final combined model, including the Rad-score of the selected features and clinical risk factors, was established. Receiver Operating Characteristic (ROC) curve analysis, Delong test, and Decision Curve Analysis (DCA) were used to assess the predictive performance of the clinical and radiomics models.

5 features were selected to construct the AP radiomics model of contrast-enhanced CT. The radiomics model of AP from contrast-enhanced CT was superior to the clinical model of AFP in training cohorts (P < 0.001), but not superior to the clinical model in validation cohorts (P = 0.151). The combined model (AUC = 0.867 vs. 0.895), including AP Rad-score and serum Alpha-Fetoprotein (AFP) levels, improved the predictive performance more than the AFP model (AUC = 0.651 vs. 0.718) in the training and validation cohorts. The combined model, with a higher decision curve indicating more net benefit, exhibited a better predictive performance than the AP radiomics model. DCA revealed that at a range threshold probability approximately above 60%, the combined model added more net benefit compared to the AP radiomics model of contrast-enhanced CT.

A combined model including AP Rad-score and serum AFP levels based on contrast-enhanced CT could preoperatively predict GPC3-positive expression in HCC.

Pelvic floor dysfunction is characterized by incomplete fecal defecation, negatively affecting the quality of life. Magnetic resonance defecography (MRD) is a useful examination that is ionizing radiation-free and easily reproducible, and provides anatomical and functional details that are obtainable through multiplanar and dynamic examinations. The study aims to detect pathology using MRD in patients with suspected pelvic floor dysfunction and determine its cause.

MRD was performed on 79 individuals. Dynamic images were obtained at rest, straining, and during defecation. Pelvic hiatus mediolateral diameters were compared between groups.

The defecation phase provided more accurate results than the straining phase for determining the existence and severity of pathology significantly.

The defecation phase is the most accurate phase for identifying the existence and severity of pathology, as the pelvic hiatus mediolateral diameter is thought to be an important factor in triggering pelvic dysfunction.

Intracranial hypertension (IH) is a neurological disease characterized by increased intracranial pressure. Idiopathic intracranial hypertension (IIH) is characterized by increased intracranial pressure without an underlying neuroradiological cause (1-3). The IH associated with a reason such as a mass, hydrocephalus, or drug use, is referred to as secondary intracranial hypertension (SIH). We aimed to detect and determine whether the increased intracranial pressure causes a change in the diffusion values of the brain in the diffusion MRI images.

The study includes 24 consecutive patients diagnosed with IIH and 18 consecutive patients diagnosed with secondary intracranial hypertension (SIH). The control group included 24 patients. Measurement of apparent diffusion coefficient (ADC) was performed using DWI sections obtained from subcortical white matter and the cortex of the frontal lobe in the basal ganglia plane, caudate nucleus head, thalamus, the posterior leg of the internal capsule, corpus callosum splenium; in the centrum semiovale plane, from the central white matter region. with 1.5T MRI using b=500s/mm² and b=1000s/mm² values both in patients and control groups. Mean ADC values were compared between IIH, SIH patients and control groups.

The ADC values from the head of the caudate nucleus and the cortex were significantly higher in the IIH group compared to the control group. When the ADC values of the SIH and control groups were compared, it was found that some of the ADC measurements (subcortical white matter, cortex and semioval center) were significantly different. The comparison of the IIH and the SIH groups revealed that the ADC measurements of central white matter in the centrum semiovale, the subcortical white matter and the posterior leg of the internal capsule were significantly different.

We have found increased diffusion of IIH and SIH patients, which supports the development of brain edema. Even though the mechanism of the brain edema in IIH is not entirely clear, it is thought that the mechanism is different from the brain edema caused by a mass or a sinus thrombosis.

Hepatic perivascular epithelioid cell tumors (PEComa) often mimic hepatocellular carcinoma (HCC) in patients without cirrhosis. This study aimed to develop a nomogram using imaging characteristics on Gd-EOB-DTPA-enhanced MRI and to distinguish PEComa from HCC in a non-cirrhotic liver.

Forty patients with non-cirrhotic Gd-EOB-DTPA-enhanced magnetic resonance imaging (MRI) were included in our study. A multivariate logistic regression model was used to select significant variables to distinguish PEComa from HCC. A nomogram was developed based on the regression model. The performance of the nomogram was assessed with respect to the ROC curve and calibration curve. Decision curve analysis (DCA) was performed to evaluate the clinical usefulness of the nomogram.

Two significant predictors were identified: the appearance of an early draining vein and the T1D value of tumors. The ROC curve showed that the area under the curve (AUC) of the model to predict the risk of PEComa was 0.91 (95% CI: 0.80~1) and showed that the model had high specificity (92.3%) and sensitivity (88.9%). The nomogram incorporating these two predictors showed favorable calibration, which was validated using 1000 resampling procedures, and the corrected C-index of this model was 0.90. Furthermore, DCA analysis showed that the model had clinical practicability.

In conclusion, the nomogram model showed favorable predictive accuracy for distinguishing PEComa from HCC in non-cirrhotic patients and may aid in clinical decision-making.

Atypical carcinoid (AC) is one of the rarest lung neuroendocrine tumors (NETs) that rarely metastasize to the breast, and only a few cases have been reported in the literature. Positron emission tomography/computed tomography (PET/CT) with somatostatin analogs (SSAs) labeled with Gallium-68 (⁶⁸Ga) now represents the gold standard for diagnosis and management of NETs. A case of an unusual metastasis to the breast from an AC detected by ⁶⁸Ga-DOTATATE PET/CT was reported.

A 73-year-old woman was presented with a right breast lesion found on mammography screening, which revealed a metastatic neuroendocrine tumor by histopathological analysis with a tru-cut biopsy. Subsequently, ⁶⁸Ga-DOTATATE PET/CT imaging performed for the initial evaluation showed increased radiotracer uptake in the lesion in the right breast as well as the nodular lesion in the middle lobe of the right lung, which was histologically confirmed to be AC.

Metastasis of uncommon AC of the lung to the breast is extremely rare. However, it is essential to properly differentiate metastatic tumors from primary disease due to differences in clinical management and prognosis, and ⁶⁸Ga-DOTATATE PET/CT is a unique diagnostic tool with the advantage of whole-body imaging.

The human skeletal system relies heavily on the integrity of bones, which provide structural support and safeguard vital organs. Accurate detection is paramount for effective diagnosis. Conventional methods for identifying fractures manually are not only time-consuming but also susceptible to errors.

The proposed methodology hinges on a patch antenna operating at 2.4 GHz and a bone phantom housing a simulated fracture, where the antenna is scanned. The collected signals are then processed with Delay-and-Sum (DAS), and Delay-Multiply-and-Sum (DMAS) reconstruction algorithms. The resulting images offer visual insights into the location of fractures.

Through experimentation, the efficacy of the images varies considerably in terms of their capacity for noise and artifact suppression. While DAS exhibits reasonable effectiveness, it suppresses noise and artifacts comprehensively. In contrast, DMAS offers clearer and more precise images of bone fractures.

In summary, the research introduces a cost-effective and non-invasive strategy for detecting bone fractures. By involving a patch antenna at 2.4 GHz, along with image reconstruction algorithms like DMAS and DAS, one can effectively visualize the location of bone fractures. The experimental results highlight the superiority of DMAS over DAS in terms of contrast resolution, making it a highly promising avenue for fracture detection.