Current Medical Imaging - Volume 20, Issue 1, 2024

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.