Current Medical Imaging - Volume 20, Issue 1, 2024

The objective of this study was to evaluate the feasibility of weight-based tube voltage and iodine delivery rate (IDR) for coronary artery CT angiography (CCTA).

A total of 193 patients (mean age: 58 ± 12 years) with suspected coronary heart disease indicated for CCTA between May and October 2022 were prospectively enrolled. The subjects were divided into five groups according to body weight: < 60 kg, 60 – 69 kg, 70 – 79 kg, 80 – 89 kg, and ≥ 90 kg. The tube voltage and IDR settings of each group were as follows: 70 kVp/0.8 gI/s, 80 kVp/1.0 gI/s, 80 kVp/1.1 gI/s, 100 kVp/1.5 gI/s, and 100 kVp/1.5 gI/s, respectively. Objective image quality data included the CT value and standard deviation (noise) of the aortic root (AR), the proximal left anterior descending branch (LAD), and the distal right coronary artery (RCA), as well as the signal-to-noise ratio and contrast-to-noise ratio of the LAD and RCA. Subjective image quality assessment was performed based on the 18-segment model. Contrast and radiation doses, as well as effective dose (ED), were recorded. All continuous variables were compared using either the one-way ANOVA or the Kruskal-Wallis rank sum test.

No significant differences were observed in all objective and subjective parameters of image quality between the groups (P > 0.05). However, significant differences in contrast and radiation doses were observed (P < 0.05). The contrast doses across the weight groups were 27 mL, 35 mL, 38 mL, 53 mL, and 53 mL, respectively, while the ED were 1.567 (1.30, 2.197) mSv, 1.53 (1.373, 1.78) mSv, 2.113 (1.963, 2.256) mSv, 4.22 (3.771, 4.483) mSv, and 4.786 (4.339, 5.536) mSv, respectively.

Weight-based tube voltage and IDR yielded consistently high image quality, and allowed for further reduction in contrast and radiation exposure during CCTA for coronary artery diseases.

To develop and validate a radiomics-clinical nomogram for the detection of the acquired T790M mutation in patients with advanced non-small cell lung cancer (NSCLC) with resistance after the duration of first-line epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitor (TKI) treatment.

Thoracic CT was collected from 120 advanced NSCLC patients who suffered progression on first- or second-generation TKIs. Radiomics signatures were retrieved from the entire tumor. Pearson correlation and the least absolute shrinkage and selection operator (LASSO) regression method were adopted to choose the most suitable radiomics features. Clinical and radiological factors were assessed using univariate and multivariate analysis. Three Machine Learning (ML) models were constructed according to three classifiers, including Logistic Regression (LR), Support Vector Machine (SVM), and RandomForest (RF), combining clinical and radiomic features. A nomogram combining clinical features and the rad score signature was built. The predictive ability of the nomogram was assessed by the ROC curve, calibration curve, and decision curve analysis (DCA).

Multivariate regression analysis showed that two clinicopathological characteristics and two radiological features were highly correlated with the acquired T790M mutation, including the progression-free survival (PFS) of first-line EGFR TKIs (P = 0.029), the initial EGFR profile (P = 0.01), vascular convergence (P = 0.043), and air bronchogram (P = 0.030). The AUCs of clinical, radiomics, and combined models using RF classifiers for T790M mutation detection were 0.951 (95% confidence interval [CI] 0.911,0.991), 0.917 (95%CI 0.856,0.978), and 0.961 (95%CI 0.927,0.995) in the training cohort, respectively, higher than those of other classifier models.The calibration curve and Hosmer-Lemeshow Test showed good calibration power, and the DCA demonstrated a significant net benefit.

A radiomics-clinical nomogram based on CT radiomics proved valuable in non-invasively and efficiently predicting the acquired T790M mutation in patients who suffered progression on first-line TKIs.

Magnetic Resonance Imaging (MRI) plays a role in demonstrating substantial utility in lung lesion imaging, detection, diagnosis, and evaluation. Previous studies have found that free-breathing star VIBE sequences not only have high image quality but also have a high ability to detect and display nodules. However, in our routine clinical practice, we have encountered suboptimal image quality in the free-breathing sequences of certain patients.

This study aims to assess the impact of breath training on the quality of chest magnetic resonance imaging obtained during free-breathing sequences.

A total of 68 patients with lung lesions, such as nodules or masses detected via Computed Tomography (CT) examination, were prospectively gathered. They were then randomly divided into two groups: an observation group and a control group. Standard preparation was performed for all patients in both groups before the examination. The observation group underwent 30 minutes of breath training prior to the MRI examination additionally, followed by the acquisition of MRI free-breathing sequence images. The signal intensity (SI) and standard deviation (SD) of the lesion and adjacent normal lung tissue were measured, and the image signal-to-noise ratio (SNR) and contrast signal-to-noise ratio (CNR) of the lesion were calculated for objective image quality evaluation. The subjective image quality of the two groups of images was also evaluated using a 5-point method.

MRI examinations were completed in both groups. Significantly better subjective image quality (edge and internal structure clarity, vascular clarity, breathing and cardiac artifacts, and overall image quality) was achieved in the observation group compared to the control group (P<0.05). In addition, higher SNR and CNR values for disease lesions were observed in the observation group compared to the control group (t=4.35, P<0.05; t=5.35, P<0.05).

It is concluded that the image quality of free-breathing sequences MRI can be improved through breath training before examination.

The visualization and understanding of the details of the root configurations and root canal structure are essential prior to root canal treatment. This study aimed to identify key indicators of pronounced root divergence between the distobuccal and distolingual roots in mandibular first molars by highlighting common features observed in panoramic radiographs. These indicators can help predict the likelihood of encountering significant root divergence before initiating endodontic treatment.

We present three cases in which panoramic radiographs displayed imaging features characteristic of root resorption in the distal root of the mandibular first molars. However, subsequent periapical radiographs in case 1 and cone-beam computed tomography images in cases 2 and 3 revealed that the mandibular first molars were in normal condition, with pronounced root divergence but no evidence of root resorption.

Panoramic radiographs depicting mandibular molar roots with a resorptive and unclear appearance may indicate the presence of severe root divergence. In such cases, we strongly recommend additional cone-beam computed tomographic imaging to ensure precise diagnosis and facilitate optimal treatment planning for endodontic procedures.

In recent years, automated grading of knee osteoarthritis (KOA) has focused on determining disease progression. Clinical examinations and radiographic image review are necessary for diagnosis. Timely and accurate diagnosis, along with medical care, can slow down KOA progression. X-rays and MRI are crucial diagnostic tools. KOA diagnosis traditionally relies on radiologists' and clinicians' experience. However, the rapid development of deep learning technology (AI) offers promising solutions for medical applications.

The objective of this study was to review and summarize various methods proposed by researchers for the automated grading of KOA. Additionally, this study aimed to evaluate the performance of the AlexNet model in classifying the severity of KOA. The performance of the AlexNet model has been compared to that of other models, and the results have been assessed.

A comprehensive review of existing research on automated grading of KOA has been conducted. Various methods proposed by different researchers have been examined and summarized. The AlexNet model has been employed for classifying the severity of KOA, and its performance has been evaluated. A comparative analysis has been carried out to compare the performance of the AlexNet model with that of other models. The results obtained from the evaluation have been assessed to determine the effectiveness of the AlexNet model in the automated grading of KOA.

The results of the study indicate that the AlexNet model demonstrates promising performance in classifying the severity of KOA. Comparative analysis reveals that the AlexNet model outperforms other models in terms of accuracy and efficiency. The evaluation of the model's performance provides valuable insights into the effectiveness of deep learning techniques for automated grading of KOA.

This study highlights the significance of automated grading in the diagnosis and management of knee osteoarthritis. The utilization of deep learning technology, particularly the AlexNet model, shows promise in accurately classifying the severity of KOA. The findings suggest that automated grading methods can serve as valuable tools for healthcare professionals in assessing the progression of KOA and providing appropriate medical care. Further research and development in this area can contribute to enhancing the efficiency and accuracy of automated grading systems for KOA.

The diagnosis and characterization of vertebral compression fractures are very important for clinical management. In this evaluation, which is usually performed with diagnostic (conventional) imaging, the findings are not always typical or diagnostic. Therefore, it is important to have new information to support imaging findings. Texture analysis is a method that can evaluate information contained in diagnostic images and is not visually noticeable. This study aimed to evaluate the magnetic resonance images of cases diagnosed with vertebral compression fractures by the texture analysis method, compare them with histopathological data, and investigate the effectiveness of this method in the differentiation of benign and malignant vertebral compression fractures.

Fifty-five patients with a total of 56 vertebral compression fractures were included in the study. Magnetic resonance images were examined and segmented using Local Image Feature Extraction (LIFEx) software, which is an open-source program for texture analysis. The results were compared with the histopathological diagnosis.

The application of the Decision Tree algorithm to the dataset yielded impressively accurate predictions (≈95% in accuracy, precision, and recall).

Interpreting tissue analysis parameters together with conventional magnetic resonance imaging findings can improve the abilities of radiologists, lead to accurate diagnoses, and prevent unnecessary invasive procedures. Further prospective trials in larger populations are needed to verify the role and performance of texture analysis in patients with vertebral compression fractures.

Whole-body bone scanning is a nuclear medicine technique with high sensitivity used for the diagnosis of bone-related diseases [e.g., bone metastases] that can be obtained by positron emission tomography (PET) or single-photon emission computed tomography[SPECT] imaging, depending on the different radiopharmaceuticals used. In contrast to the high sensitivity of the bone scan, it has low specificity, which leads to misinterpretation, causing adverse effects of unwarranted intervention or interruption to timely treatment.

To address this problem, this paper proposes a joint model called mSegResRF-SPECT, which accomplishes for the first time the task of classifying whole-body bone scan images on a public SPECT dataset [BS-80K] for the diagnosis of bone metastases.

The mSegResRF-SPECT adopts a multi-bone region segmentation algorithm to segment the whole body image into 13 regions, ResNet34 as an extractor to extract the regional features, and a random forest algorithm as a classifier.

The experimental results of the proposed model show that the average accuracy, sensitivity, and F1 score of the model on the BS-80K dataset reached SOTA.

The proposed method presents a promising solution for better bone scan classification methods.

This study explored whether breast MRI manifestations could be used to predict the stroma distribution of breast cancer (BC) and the role of tumor stroma-based MRI manifestations in molecular subtype prediction.

57 patients with pathologically confirmed invasive BC (non-special type) who had lumpy BC on MRI within one week before surgery were retrospectively collected in the study. Stroma distributions were classified according to their characteristics in the pathological sections. The stromal distribution patterns among molecular subtypes were compared with the MRI manifestations of BC with different stroma distribution types (SDTs).

SDTs were significantly different and depended on the BC hormone receptor (HR) (P<0.001). There were also significant differences among five SDTs on T2WI, ADC map, internal delayed enhanced features (IDEF), marginal delayed enhanced features (MDEF), and time signal intensity (TSI) curves. Spiculated margin and the absence of type-I TSI were independent predictors for BC with star grid type stroma. The appearance frequency of hypo-intensity on T2WI in HR- BCs was significantly lower (P=0.043) than in HR+ BCs. Star grid stroma and spiculated margin were key factors in predicting HR+ BCs, and the AUC was 0.927 (95% CI: 0.867-0.987).

Breast MRI can be used to predict BC's stromal distribution and molecular subtypes.

Interstitial lung abnormalities (ILA) are associated with further disease progression, increased mortality risk, and decline in lung function in the elderly, which deserves enough attention.

The objective of this study was to quantify the extent of interstitial lung abnormalities (ILA) in a non-smoking asymptomatic urban cohort in China using low-dose CT (LDCT) and to analyze the age-related pathological changes.

We retrospectively analyzed clinical data and chest LDCT images from a cohort of 733 subjects who were categorized into 3 groups: 18–39, 40-59, and ≥60 years old according to age. Furthermore, we selected 40 cases of wax-embedded lung tissue blocks archived after pulmonary bullectomy and the same age groups were categorized. Four representative CT signs of ILA, including interlobular septal thickening (ILST), intralobular interstitial thickening (ILIT), ground-glass opacity (GGO), and reticular shadow (RS), were semi-quantified based on the percentage of the affected area. The scores and distribution of four CT signs of ILA were compared between different sex and age groups. The age-related pathological changes were analyzed.

The ILA findings were found predominantly in the lower lobes and the subpleural region. The semi-quantitative scores of four CT signs in all subjects under 40 were 0. However, in subjects over 40 years old, the scores gradually increased with age, although most of them remained low. The size of the alveoli increased, the number of alveoli decreased, the alveolar septum became thinner, and the number of ATII cells increased with age. A statistically significant difference was observed among the different age groups (χ2=50.624, P=0.033; χ2=80.000, P=0.043; χ2=33.833, P=0.000; χ2=13.525, P=0.031). The macrophage population and the percentage of collagen fibers in the alveolar septum increased, while the percentage of elastic fibers decreased with age. There was no significant difference among the different age groups (χ2=19.817, P=0.506; χ2=52.419, P=0. 682; χ2=54.868, P=0.518).

When the four CT signs mentioned above are in the upper central area, and the score has a medium or high score, it is crucial to determine the underlying pathological causes. ILA may be the result of chronic lung injury.

Hepatic portal venous gas (HPVG) is very rare; it is associated with multiple gastrointestinal etiologies, with pathophysiology not yet fully understood. It is characteristically fast-progressing and has a high mortality rate. Treatment choice depends on the etiology, including conservative and surgical management.

We report an adult patient (less than 25 years old) of HPVG combined with acute upper gastrointestinal hemorrhage, in which massive gas in the hepatic portal vein system by computed tomography of the abdomen was rapidly dissipated by nasogastric decompression conservative management.

Nasogastric decompression can be an effective treatment approach for HPVG when timely surgical treatment is not required.

Fat-suppressed (FS) T2-weighed turbo spin-echo (TSE) sequence was used to detect the signal of the thymus and the characteristics of the thymus location, measure the two-dimensional diameter at specific levels, and analyze the association with gestational weeks.

This study involved 51 fetal specimens. Post-mortem MRI scanning was implemented with a 3.0-T MRI system. T2-weighted imaging (T2WI) features of the thymus in fetuses were quantitatively investigated with DICOM images. Statistical analysis was done with the Chi-Square test, one-way ANOVA, and Student’s t-test.

There was heterogeneity in the morphology of the fetal thymus. FS T2-weighted TSE sequence clearly exhibited the microstructure of the fetal thymus. The thymus extensively showed a lobulated appearance. The central signal is much higher than the peripheral signal in each lobule. In addition, FS-T2WI images can clearly show the interlobular septum, which is filled with fluid and presents a linear high signal. The signal intensity of fetal thymus increased with gestational weeks. The diameter measured in a particular plane was highly correlated with gestational week.

FS T2-weighted TSE sequence provides high-resolution images of the fetal thymus. The change in signal intensity, location, and two-dimensional diameter in a specific plane can be used as a research direction for the fetal thymus.

Mesenchymal tumours of the bladder are benign but rare occurrences and represent approximately 1% of all bladder tumours.

We report a case of a large bladder leiomyoma in an asymptomatic patient. A large pelvic mass was discovered incidentally on the bedside ultrasound scan during a review at the gynecology clinic. Intra-operatively, no mass was seen in the pelvis, and cystoscopy demonstrated an intravesical mass. It was further evaluated with cystoscopy. MR imaging demonstrated typical features of a bladder leiomyoma. Subsequently, the patient underwent partial cystectomy, and the mass was removed, which was histologically proven leiomyoma.

Awareness of this rare clinical entity and identification of its typical radiological features on MR imaging can aid with accurate diagnosis and preclude unnecessary radical surgery.

The aim of this study was to develop an algorithm model to predict the heat sink effect during thermal ablation of lung tumors and to assist doctors in the formulation and adjustment of surgical protocols.

The heat sink effect is an important factor affecting the therapeutic effect of tumor thermal ablation. At present, there is no algorithm model to predict the intraoperative heat sink effect automatically, which needs to be measured manually, which lacks accuracy and consumes time.

To construct a segmentation model based on a convolutional neural network that can automatically identify and segment pulmonary nodules and vascular structure and measure the distance between the nodule and vascular.

First, the classical Faster RCNN model was used as the nodule detection network. After obtaining the bounding box of pulmonary nodules, the VSPP-NET model was used to segment nodules in the bounding box. The distance from the nodule to the vasculature was measured after the surrounding vasculature was segmented by the VSPP-NET model. The lung CT images of 392 patients with pulmonary nodules were used as the training data for the algorithm. 68 cases were used as algorithm validation data, 29 as nodule algorithm test data, and 80 as vascular algorithm test data. We compared the heat sink effect of 29 cases of data with the results of the algorithm model and expert segmentation and compared the difference between the two results.

In pulmonary CT image vasculature segmentation, the recall and precision of the algorithm model reached >0.88 and >0.78, respectively. The average time for automatic segmentation of each image model is 29 seconds, and the average time for manual segmentation is 158 seconds. The output image of the model shows that the results of nodule segmentation and nodule distance measurement are satisfactory. In terms of heat sink effect prediction, the positive rate of the algorithm group was 28.3%, and that of the expert group was 32.1%, with no significant difference between the two groups (p=0.687).

The algorithm model developed in this study shows good performance in predicting the heat sink effect during pulmonary thermal ablation. It can improve the speed and accuracy of nodule and vessel segmentation, save ablation planning time, reduce the interference of human factors, and provide more reference information for surgeons to make ablation plans to improve the ablation effect.

Currently, coronavirus disease 2019 (COVID-19) continues to remain in the pandemic stage, leading to severe challenges in the global public healthcare system. Magnetic resonance imaging (MRI) methods have played an important role in the diagnosis of COVID-19 and the structural evaluation of the affected organs. Reviewing and summarizing the application of MRI has significant clinical implications for COVID-19. Objective: The study aimed to analyze literature related to the application of MRI in COVID-19 using bibliometric tools, to explore the research status, hotspots, and developmental trends in this field, and to provide a reference for the application of MRI in the clinical diagnosis and evaluation of COVID-19.

We used the Web of Science Core Collection database to search and collect relevant literature on the use of MRI in COVID-19. The authors, institutes, countries, journals, and keyword modules of the bibliometric analysis software CiteSpace and VOSviewer were used to analyze and plot the network map.

A total of 1506 relevant articles were shortlisted through the search; the earliest study was published in 2019, showing an overall upward trend every year. The research was mainly presented as published articles. Clinical neurology was found to be the primary discipline. The United States had the highest publication volume and influence in this field. Countries around the world cooperated more closely. The Cureus Journal of Medical Science was the main periodical to publish articles. Institutes, such as Harvard Medical School, Mayo Clinic, and Massachusetts General Hospital, have published a large number of papers. Some of the high-frequency keywords were “COVID-19”, “SARS-CoV-2”, “magnetic resonance”, “myocarditis”, and “cardiac magnetic resonance imaging”. The keyword clustering study showed that the current research mainly focuses on five “hot” directions.

There is a need to strengthen cross-teamwork and multidisciplinary collaboration in the future to completely explore the positive role of MRI in COVID-19 and to discover breakthroughs for the challenges in the clinical diagnosis and treatment of COVID-19.

Interstitial lung diseases (ILD) encompass various disorders characterized by inflammation and/or fibrosis in the lung interstitium. These conditions produce distinct patterns in High-Resolution Computed Tomography (HRCT).

We employ a deep learning method to diagnose the most commonly encountered patterns in ILD differentially.

Patients were categorized into usual interstitial pneumonia (UIP), nonspecific interstitial pneumonia (NSIP), and normal lung parenchyma groups. VGG16 and VGG19 deep learning architectures were utilized. 85% of each pattern was used as training data for the artificial intelligence model. The models were then tasked with diagnosing the patterns in the test dataset without human intervention. Accuracy rates were calculated for both models.

1 The success of the VGG16 model in the test phase was 95.02% accuracy. 2 Using the same data, 98.05% accuracy results were obtained in the test phase of the VGG19 model.

Deep Learning models showed high accuracy in distinguishing the two most common patterns of ILD.

At present, there are some problems in multimodal medical image fusion, such as texture detail loss, leading to edge contour blurring and image energy loss, leading to contrast reduction.

To solve these problems and obtain higher-quality fusion images, this study proposes an image fusion method based on local saliency energy and multi-scale fractal dimension.

First, by using a non-subsampled contourlet transform, the medical image was divided into 4 layers of high-pass subbands and 1 layer of low-pass subband. Second, in order to fuse the high-pass subbands of layers 2 to 4, the fusion rules based on a multi-scale morphological gradient and an activity measure were used as external stimuli in pulse coupled neural network. Third, a fusion rule based on the improved multi-scale fractal dimension and new local saliency energy was proposed, respectively, for the low-pass subband and the 1^st closest to the low-pass subband. Layer-high pass sub-bands were fused. Lastly, the fused image was created by performing the inverse non-subsampled contourlet transform on the fused sub-bands.

On three multimodal medical image datasets, the proposed method was compared with 7 other fusion methods using 5 common objective evaluation metrics.

Experiments showed that this method can protect the contrast and edge of fusion image well and has strong competitiveness in both subjective and objective evaluation.

Secondary cardiac tumors are a rare disease that is hard to detect when the tumor is small and asymptomatic. This case report focuses on a massive pulmonary metastasis filling almost the entire left atrium. Multimodal enhancement imaging, cardiac contrast-enhanced ultrasound (CEUS), enhanced electron computed tomography, and positron emission tomography imaging were applied to detect the malignant origin of this case. The aim of this project was to provide an important basis for clinical treatment and decision-making with multimodal imaging.

The patient was hospitalized with suspected to be a lumbar spine fracture. According to the multimodal imaging, pathologically confirmed to suffer a cardiac metastasis from small cell lung cancer. EP-regimen (Etoposide 0.1gd 1-5+Nedaplatin 30mgd 1-4) was selected for the systemic chemotherapy of the patient. During three years of follow-up, the left intra-atrial occupancy was significantly reduced.

Multimodality imaging can cover up the deficiencies of single imaging examinations and further clarify and enrich the understanding of the relationship between the location and the surrounding structure of the mass, thus providing a good reference for clinical treatment and decision-making.

Nowadays, High Intensity Focused Ultrasound (HIFU) is a common surgery option for the treatment of uterine fibroids in China, the immediate response of which is clinically evaluated using Contrast Enhanced (CE) imaging. However, the injection of gadolinium with its potential adverse effect is of concern in CE and therefore, it deserves efforts to find a better imaging method without the need for contrast agent injection for this task.

To assess the role of Diffusion-weighted Imaging (DWI) in evaluating the immediate therapeutic response of HIFU treatment for uterine fibroids in comparison with CE.

68 patients with 74 uterine fibroids receiving HIFU treatment were enrolled, and immediate treatment response was assessed using post-surgical DWI images. Semi-quantitative ordinal ablation quality grading and quantitative nonperfusion volume (NPV) measurement based on DWI and CE imaging were determined by two experienced radiologists. Agreement of ablation quality grading between DWI and CE was assessed using the weighted kappa coefficient, while intraobserver, interobserver and interprotocol agreements of NPV measurements within and between DWI and CE were evaluated using the intraclass correlation (ICC) and Bland-Altman analysis.

Grading of immediate HIFU treatment response showed a moderate agreement between DWI and CE (weighted kappa = 0.446, p < 0.001). NPV measured in 65 fibroids with DWI of Grade 3~5 showed very high ICCs for the intraobserver and interobserver agreement within DWI and CE (all ICC > 0.980, p < 0.001) and also for the interprotocol agreement between DWI and CE (ICC = 0.976, p < 0.001).

DWI could provide satisfactory ablation quality grading, and reliable NPV quantification results to assess immediate therapeutic responses of HIFU treatment for uterine fibroids in most cases, which suggests that non-contrast enhanced DWI might be potentially used as a more cost-effective and convenient method in a large proportion of patients for this task replacing CE imaging.

We aimed to differentiate granulosa cell tumors (GCT) from other ovarian sex-cord tumors (OSCs) based on feature analysis of the tumor body on MR imaging.

We retrospectively enrolled 27 patients with pathologically proven sex-cord tumours (14 GSTs, 8 fibromas, 4 fibrothecomas, and 1 sclerosing stromal tumour) from our institution. All MRI examinations were performed at least one month prior to surgery. MR image features were recorded by two radiologists with consensus readings. Histogram analysis was performed using FeAture Explorer software. The differences in histogram parameters between GCT (38.1 ± 14.6 years) and OSC (43.7 ± 18.0 years) groups were compared. Fourteen randomly selected cellular-type myomas who also underwent MRI in our hospital were considered as the control group. The intra-operator consistency of ADC value was evaluated across measurements twice.

The repeatability of conventional ADC measurements on the tumor body was good. The values of ADC-mean, ADC-min, and ADC-max significantly differed across three groups (p < 0.001). The histogram variance on DWI, histogram percentage on T2WI, and ADC min showed the best discriminative performance in determining GCTs from other OSCs with an area under the receiver operator curve (AUC) of 0.997, 0.882, and 0.795, respectively. The histogram variance on DWI yielded a sensitivity of 92.3%, a specificity of 100%, and an accuracy of 96.6% in discriminating GSTs from other OSCs.

In the present study, feature analysis of tumor body MR imaging has helped to differentiate GST from OSC with better performance than conventional ADC measurements.

This study is to investigate the effectiveness of Acoustic Radiation Force Impulse (ARFI) elastography in differentiating radiologically similar renal cell carcinoma (RCC) and oncocytoma in solid masses of the kidney.

The patients with solid renal mass histopathological diagnosed after excision or tru-cat biopsy who underwent a preoperative ARFI elastography of the lesion during a 4-year period were included in this study. Preoperative shear wave velocity (SWV) values were measured in all the lesions. SWV results of RCCs and oncocytomas were compared by an independent t-test, and cut-off, sensitivity and specificity values were calculated.

Forty-two of the 60 patients included in the study were men (70%) and, 18 were women (30%), and the mean age was 59.7 ± 14 (27-94) years. Among 46 RCCs (76.6%), 23 and 14 oncocytomas, 5 (23.4%) were located in the right kidney (p:0.34722). Mean SWV values were found to be significantly higher in RCCs (2.87± 0.74 (0.96-4.14) m/s) than oncocytomas (1.83 ± 0.78 (0.80-3.76) m/s) (p <0.001). In the ROC analysis, a cut-off value of 2.29 m/s was found to havean 80.4% sensitivity and a 78.6% specificity for the discrimination of RCCs from oncocytomas.

ARFI elastography measurements may be useful in distinguishing RCC and oncocytomas that may have similar solid radiological imaging features.

A contrast agent-free approach would be preferable to the frequently used invasive approaches for evaluating cerebral perfusion in chronic migraineurs (CM). In this work, non-invasive quantitative volumetric perfusion imaging was used to evaluate alterations in cerebral perfusion in CM.

We used conventional brain structural imaging sequences and 3D pseudo-continuous arterial spin labeling (3D PCASL) to examine thirteen CM patients and fifteen normal controls (NCs). The entire brain gray matter underwent voxel-based analysis, and the cerebral blood flow (CBF) values of the altered positive areas were retrieved to look into the clinical variables' significant correlation.

Brain regions with the decreased perfusion were located in the left postcentral gyrus, bilateral middle frontal gyrus, left middle occipital gyrus, left superior parietal lobule, left medial segment of superior frontal gyrus, and right orbital part of the inferior frontal gyrus. White matter fibers with decreased perfusion were located in bilateral superior longitudinal tracts, superior corona radiata, external capsules, anterior and posterior limbs of the internal capsule, anterior corona radiata, inferior longitudinal fasciculus, and right corticospinal tract. However, the correlation analysis showed no significant correlation between the CBF value of the above positive brain regions with clinical variables (p > 0.05).

The current study provided more useful information to comprehend the pathophysiology of CM and revealed a new insight into the neural mechanism of CM from the pattern of cerebral hypoperfusion.

The purpose of this article is to combine the functional information of CT images with the anatomical and soft tissue information of MRI through image fusion technology, providing more detailed information for rehabilitation treatment and thus providing a scientific basis for clinical applications and better training plans.

In this paper, functional brain imaging technology combining CT (computed tomography) and MRI (magnetic resonance imaging) was used for image fusion, and SURF (accelerated robust feature) feature points of images were extracted. In this study, 40 patients with mild and moderate closed traumatic brain injury admitted to the rehabilitation department of a rehabilitation center from 2018 to 2022 were selected as the research objects.

Compared with using only CT images and MRI images for brain injury diagnosis, the fusion image had a higher detection rate of abnormal brain injury diagnosis, with a detection rate of 97.5%. When using fused images for the diagnosis of abnormal brain injury, the patient’s exercise rehabilitation effect was better.

CT and MRI image fusion technology had a high diagnostic accuracy for brain injury, which could timely guide doctors in determining exercise rehabilitation plans and help improve the effectiveness of patient exercise rehabilitation.

In image processing, image segmentation is a more challenging task due to different shapes, locations, image intensities, etc. Brain tumors are one of the most common diseases in the world. So, the detection and segmentation of brain tumors are important in the medical field.

The primary goal of this work is to use the proposed methodology to segment brain MRI images into tumor and non-tumor segments or pixels.

In this work, we first selected the MRI medical images from the BraTS2020 database and transferred them to the contrast enhancement phase. Then, we applied thresholding for contrast enhancement to enhance the visibility of structures like blood arteries, tumors, or abnormalities. After the contrast enhancement process, the images were transformed into the image denoising phase. In this phase, a fourth-order partial differential equation was used for image denoising. After the image denoising process, these images were passed on to the segmentation phase. In this segmentation phase, we used an elephant herding algorithm for centroid optimization and then applied the multi-kernel fuzzy c-means clustering for image segmentation.

Peak signal-to-noise ratio, mean square error, sensitivity, specificity, and accuracy were used to assess the performance of the proposed methods. According to the findings, the proposed strategy produced better outcomes than the conventional methods.

Our proposed methodology was reported to be a more effective technique than existing techniques.

Fallopian tube leiomyoma is an uncommon, benign gynecologic tumor that originates from the smooth muscle of the fallopian tube or vascular cells supplying the fallopian tube.

In this study, we report a case of a patient with fallopian tube leiomyoma. What makes this instance even more unique is the association of the leiomyoma with cystic degeneration, manifesting as a large abdominopelvic cystic mass. CT scan suspected that the mass might be an ovarian cystadenoma. However, ultrasonography, a widely used diagnostic tool, effectively assisted the clinicians in confidently ruling out the possibility that the tumor was originating from the ovaries. Ultimately, the patient underwent exploratory laparoscopy and the pathologic diagnosis was fallopian tube leiomyoma with cystic degeneration. To our knowledge, no instance of a fallopian tube leiomyoma of this size with cystic degeneration has been reported. Thus, it is worth mentioning.

In summary, fallopian tube leiomyomas are classified as uncommon benign gynecologic tumors, which pose challenges in clinical diagnosis. The combined use of multiple imaging modalities may be more helpful in the proper diagnosis of this disease entity.

The performance of automatic liver segmentation and manual sampling MRI strategies needs be compared to determine interchangeability.

To compare automatic liver segmentation and manual sampling strategies (manual whole liver segmentation and standardized manual region of interest) for performance in quantifying liver volume and MRI-proton density fat fraction (MRI-PDFF), identifying steatosis grade, and time burden.

Fifty patients with obesity who underwent liver biopsy and MRI between December 2017 and November 2018 were included. Sampling strategies included automatic and manual whole liver segmentation and 4 and 9 large regions of interest. Intraclass correlation coefficient (ICC), Bland–Altman, linear regression, receiver operating characteristic curve, and Pearson correlation analyses were performed.

Automatic whole liver segmentation liver volume and manual whole liver segmentation liver volume showed excellent agreement (ICC=0.97), high correlation (R²=0.96), and low bias (3.7%, 95% limits of agreement, -4.8%, 12.2%) in liver volume. There was the best agreement (ICC=0.99), highest correlation (R²=1.00), and minimum bias (0.84%, 95% limits of agreement, -0.20%, 1.89%) between automated whole liver segmentation MRI-PDFF and manual whole liver segmentation MRI-PDFF. There was no difference of each paired comparison of receiver operating characteristic curves for detecting steatosis (P=0.07–1.00). The minimum time burden for automatic whole liver segmentation was 0.32 s (0.32–0.33 s).

Automatic measurement has similar effects to manual measurement in quantifying liver volume, MRI-PDFF, and detecting steatosis. Time burden of automatic whole liver segmentation is minimal among all sampling strategies. Manual measurement can be replaced by automatic measurement to improve quantitative efficiency.

To compare the integrity, clarity, conciseness, etc., of the structured report (SR) versus free-text report (FTR) for computed tomography enterography of Crohn’s disease (CD).

FTRs and SRs were generated for 30 patients with CD. The integrity, clarity, conciseness etc., of SRs versus FTRs, were compared. In this study, an evidence-based medicine practice model was utilized on 92 CD patients based on SR in order to evaluate its clinical value. Then, the life quality of the patients in two groups was evaluated before and after three months of intervention using an Inflammatory Bowel Disease Questionnaire (IBDQ).

SRs received higher ratings for satisfaction with integrity (median rating 4.27 vs. 3.75, P=0.008), clarity (median rating 4.20 vs. 3.43, P=0.003), conciseness (median rating 4.23 vs. 3.20, P=0.003), the possibility of contacting a radiologist to interpret (median rating 4.17 vs. 3.20, P<0.001), and overall clinical impact (median rating 4.23 vs. 3.27, P<0.001) than FTRs. Besides, research group had higher score of IBDQ intestinal symptom dimension (median score 61.13 vs. 58.02, P=0.003), IBDQ systemic symptom dimension (median score 24.48 vs. 20.67, P<0.001), IBDQ emotional capacity dimension (median score 65.65 vs. 61.74, P<0.001), IBDQ social ability dimension (median score 26.80 vs. 22.37, P<0.001), and total IBDQ score (median score 178.07 vs. 162.80, P<0.001) than control group.

The SR of CTE in CD patients was conducive to improving the quality and readability of the report, and CD patients’ life quality could significantly improve after the intervention of an evidence-based medicine model based on SR.

Zinner’s syndrome is a rare congenital malformation of the seminal vesicle and ipsilateral upper urinary tract caused by mesonephric duct developmental anomaly during early embryogenesis. This study aimed to demonstrate the significance of magnetic resonance imaging (MRI) in distinguishing pelvic cysts in males, given that MRI is the gold standard exam for confirming the diagnosis and managing therapy.

A 21-year-old male patient with a solitary kidney who had been diagnosed since birth presented with abdominal pain. Transabdominal and transrectal ultrasonography (US), computed tomography (CT), and MRI were performed. The contrast-enhanced MRI of the pelvis showed a tubular fluid-filled, macrolobulated lesion measuring 6 x 6 x 4 cm, mildly high signal intensity in the T2-weighted images, and slightly high signal intensity in the T1-weighted images, without contrast enhancement. The left kidney was hypoplasic. Imaging findings led to the diagnosis of Zinner’s syndrome, and conservative treatment was planned.

Zinner’s syndrome is characterized by a triad consisting of unilateral renal agenesis or hypoplasia, ipsilateral seminal vesicle cyst, and ipsilateral ejaculatory duct obstruction. MRI is the modality of choice for an impeccable depiction of the anatomy of the male genital tract, for demonstrating the seminal vesicles and evaluating anomalies of the mesonephric duct. It is also useful in distinguishing seminal vesicle cysts from other cystic pelvic masses.

Zinner’s syndrome should be considered when diagnosing cystic pelvic masses in males with renal agenesis or hypoplasia. Because of its high soft tissue contrast resolution, MRI is the gold standard modality for confirming the diagnosis and assessing the cyst’s origin and contents.

Patients with atherosclerosis have a rather high risk of showing complications, if not diagnosed quickly and efficiently.

In this paper we aim to test and compare different pre-trained deep learning models, to find the best model for atherosclerosis detection in coronary CT angiography.

We experimented with different pre-trained deep learning models and fine-tuned each model to achieve the best classification accuracy. We then used the Haar wavelet decomposition to improve the model’s sensitivity.

We found that the Resnet101 architecture had the best performance with an accuracy of 95.2%, 60.8% sensitivity, and 90.48% PPV. Compared to the state of the art which uses a 3D CNN and achieved 90.9% accuracy, 68.9% Sensitivity and 58.8% PPV, sensitivity was quite low. To improve the sensitivity, we chose to use the Haar wavelet decomposition and trained the CNN model with the module of the three details: Low_High, High_Low, and High_High. The best sensitivity reached 80% with the CNN_KNN classifier.

It is possible to perform atherosclerosis detection straight from CCTA images using a pretrained Resnet101, which has good accuracy and PPV. The low sensitivity can be improved using Haar wavelet decomposition and CNN-KNN classifier.

Medical imaging plays a key role in neurosurgery; thereby, imaging and analysis of the soft and hard tissues during bone grinding is of paramount importance for neurosurgeons. Bone grinding, a minimally invasive operation in the field of neurosurgery amid osteotomy, has been used during brain cancer surgery.

With increasing attention to neural tissue damage in machining operations, imaging of these neural tissues becomes vital and reducing temperature is imperative.

In the present study, a novel attempt has been made to perform the imaging of bone tissues during the bone grinding procedure and further investigate the relationship between rotational speed, feed rate, depth of cut with cutting forces, and temperature. The role of cutting forces and temperature has been addressed as per the requirements of neurosurgeons. Firstly, a three-factor, three-level design was constructed with a full factorial design. Regression models were employed to construct the models between input parameters and response characteristics. Medical imaging techniques were used to perform a thorough analysis of thermal necrosis and damage to the bone. Subsequently, the non-dominated sorting genetic algorithm (NSGA-III) was used to optimize the parameters for reduction in the cutting forces and temperature during bone grinding while reducing neural tissue damage.

The results revealed that the maximum value of tangential force was 21.32 N, thrust force was 9.25 N, grinding force ratio was 0.453, torque was 4.55 N-mm, and temperature was 59.3°C. It has been observed that maximum temperature was generated at a rotational speed of 55000 rpm, feed rate of 60 mm/min, and depth of cut of 1.0 mm. Histopathological imaging analysis revealed the presence of viable lacunas, empty lacunas, haversian canals, and osteocytes in the bone samples. Furthermore, the elemental composition of the bone highlights the presence of carbon (c) 59.49%, oxygen (O) 35.82%, sodium (Na) 0.11%, phosphorous 1.50%, sulphur 0.33%, chlorine 0.98%, and calcium 1.77%.

The study revealed that compared to the initial scenario, NSGA-III can produce better results without compromising the trial results. According to a statistical study, the rise in temperature during bone grinding was significantly influenced by rotating speed. The density of osteocytes in the lacunas was higher at lower temperatures. Furthermore, the results of surface electron microscopy and energy dispersive spectroscopy revealed the presence of bone over the surface of the grinding burr, which resulted in the loading of the grinding burr. The results of the present investigation will be beneficial for researchers and clinical practitioners worldwide.

The primary objective of this comparative investigation was to examine the qualitative attributes of image reconstructions utilizing two distinct algorithms, namely OSEM and HYPER Iterative, in total-body 18F- FDG PET/CT under various acquisition durations and injection activities.

An initial assessment was executed using a NEMA phantom to compare image quality engendered by OSEM and HYPER Iterative algorithms. Parameters such as BV, COV, and CRC were meticulously evaluated. Subsequently, a prospective cohort study was conducted on 50 patients, employing both reconstruction algorithms. The study was compartmentalized into distinct acquisition time and dosage groups. Lesions were further categorized into three size-based groups. Quantifiable metrics including SD of noise, SUVmax, SNR, and TBR were computed. Additionally, the differences in values, namely ΔSUVmax, ΔTBR, %ΔSUVmax, %ΔSD, and %ΔSNR, between OSEM and HYPER Iterative algorithms were also calculated.

The HYPER Iterative algorithm showed reduced BV and COV compared to OSEM in the phantom study, with constant acquisition time. In the clinical study, lesion SUVmax, TBR, and SNR were significantly elevated in images reconstructed using the HYPER Iterative algorithm in comparison to those generated by OSEM (p < 0.001). Furthermore, an amplified increase in SUVmax was predominantly discernible in lesions with dimensions less than 10 mm. Metrics such as %ΔSNR and %ΔSD in HYPER Iterative exhibited improvements correlating with reduced acquisition times and dosages, wherein a more pronounced degree of enhancement was observable in both ΔSUVmax and ΔTBR.

The HYPER Iterative algorithm significantly improves SUVmax and reduces noise level, with particular efficacy in lesions measuring ≤ 10 mm and under conditions of abbreviated acquisition times and lower dosages.

In the realm of cancer studies,the differences among the biological behavior of oligometastatic prostate cancer (OPCa), localized prostate cancer (LPCa), and widely prostate cancer (WPCa) are still unclear.

The purpose of our study was to assess the clinical and intravoxel incoherent motion (IVIM) parameters of tumor burden in OPCa. In addition, the correlation between clinical and IVIM parameters and the prostate-specific antigen nadir (PSAN) and time to nadir (TTN) during initial androgen deprivation therapy (ADT) in OPCa was explored. It was found that the IVIM parameters could effectively differentiate LPCa and WPCa, as well as LPCa and OPC. Moreover, Gleason score (GS) was positively correlated with PSAN, while prostate volume was positively correlated with TTN.

About 54 patients were included in this retrospective study (mean age=74±7.4 years). ADC, D, D*, and f were acquired according to the biexponential Diffusion Weighted Imaging (DWI) model. The Kruskal-Wallis test was used to test the differences in clinical and IVIM parameters among the three groups. The Receiver Operating Characteristic (ROC) curve was used to evaluate the discrimination abilities. The Area Under the Curve (AUC) was compared using the DeLong test. Furthermore, Spearman correlation analysis was performed to assess the correlation between clinical and IVIM parameters of PSAN and TTN during initial ADT with OPCa.

There were significant differences among the three groups observed for age, PSA, GS, ADC, D and D* values (P<0.05). Multi-parameter pairwise comparison results showed that significant differences between LPCa and WPCa were observed for the age, PSA, GS, ADC, D and D* values (P<0.05). However, D* was different between the LPCa and OPCa groups (P=0.032). GS showed a significant positive correlation with PSAN (Rho=0.594, P=0.042), and prostate volume showed a significant positive correlation with TTN (Rho=0.777, P=0.003).

The IVIM parameters can effectively differentiate LPCa and WPCa, as well as LPCa and OPCa. Moreover, there was a certain trend in their distribution, which could reflect the tumor burden of PCa.

Plunging ranula is a variant of ranula, which present as a painless subcutaneous anterolateral neck mass and is located beyond the mylohyoid muscle. Plunging ranula is a diagnostic challenge and can present with intraoral component.

An elderly male presented with painless neck mass in the cervical region for three months. The mass was excised, and the patient is doing well on follow-up. We report a case of recurrent plunging ranula without any intraoral component.

Whenever the intraoral component is missing in ranula, chances of misdiagnosis and mismanagement are high. Awareness of this entity and high index of suspicion is needed for accurate diagnosis and effective management.

Cancer is a major disease that threatens human life and health. Raman spectroscopy can provide an effective detection method.

The study aimed to introduce the application of Raman spectroscopy to tumor detection. We have introduced the current mainstream Raman spectroscopy technology and related application research.

This article has first introduced the grim situation of malignant tumors in the world. The advantages of tumor diagnosis based on Raman spectroscopy have also been analyzed. Secondly, various Raman spectroscopy techniques applied in the medical field are introduced. Several studies on the application of Raman spectroscopy to tumors in different parts of the human body are discussed. Then the advantages of combining deep learning with Raman spectroscopy in the diagnosis of tumors are discussed. Finally, the related problems of tumor diagnosis methods based on Raman spectroscopy are pointed out. This may provide useful clues for future work.

Raman spectroscopy can be an effective method for diagnosing tumors. Moreover, Raman spectroscopy diagnosis combined with deep learning can provide more convenient and accurate detection results.

Tetralogy of Fallot (ToF) is the most common form of cyanotic congenital heart disease, where right ventricular (RV) function is an important determinant of subsequent intervention.

In this study, we evaluate the feasibility of fast strain-encoding (fastSENC; a one-heartbeat sequence) magnetic resonance imaging (MRI) for assessing regional cardiac function in ToF.

FastSENC was implemented to characterize regional circumferential (Ecc) and longitudinal (Ell) strains in the left ventricle (LV) and RV in post-repair ToF. Data analysis was conducted to compare strain measurements in the RV to those in the LV, as well as to those generated by the MRI Tissue-Tracking (MRI-TT) technique, and to assess the relationship between strain and ejection fraction (EF).

Despite normal LVEF (55±8.5%), RVEF was borderline (46±6.4%), but significantly lower than LVEF. RV strains (RV-Ell=-20.2±2.9%, RV-Ecc=-15.7±6.4%) were less than LV strains (LV-Ell=-21.7±3.7%, LV-Ecc=-18.3±4.7%), and Ell was the dominant strain component. Strain differences between fastSENC and MRI-TT were less significant in RV than in LV. There existed moderate and weak correlations for RV-Ecc and RV-Ell, respectively, against RVEF. Compared to LV strain, RV strain showed regional heterogeneity with a trend for reduced strain from the inferior to anterior regions. Inter-ventricular strain delay was larger for Ell (64±47ms) compared to Ecc (36±40ms), reflecting a trend for contraction dyssynchrony.

FastSENC allows for characterizing subclinical regional RV dysfunction in ToF. Due to its sensitivity for evaluating regional myocardial contractility patterns and real-time imaging capability without the need for breath-holding, fastSENC makes it more suitable for evaluating RV function in ToF.

Accurate prediction of recurrence risk after resction in patients with Hepatocellular Carcinoma (HCC) may help to individualize therapy strategies. This study aimed to develop machine learning models based on preoperative clinical factors and multiparameter Magnetic Resonance Imaging (MRI) characteristics to predict the 1-year recurrence after HCC resection.

Eighty-two patients with single HCC who underwent surgery were retrospectively analyzed. All patients underwent preoperative gadoxetic acid-enhanced MRI examination. Preoperative clinical factors and MRI characteristics were collected for feature selection. Least Absolute Shrinkage and Selection Operator (LASSO) was applied to select the optimal features for predicting postoperative 1-year recurrence of HCC. Four machine learning algorithms, Multilayer Perception (MLP), random forest, support vector machine, and k-nearest neighbor, were used to construct the predictive models based on the selected features. A Receiver Operating Characteristic (ROC) curve was used to assess the performance of each model.

Among the enrolled patients, 32 patients experienced recurrences within one year, while 50 did not. Tumor size, peritumoral hypointensity, decreasing ratio of liver parenchyma T1 value (ΔT1), and α-fetoprotein (AFP) levels were selected by using LASSO to develop the machine learning models. The area under the curve (AUC) of each model exceeded 0.72. Among the models, the MLP model showed the best performance with an AUC, accuracy, sensitivity, and specificity of 0.813, 0.742, 0.570, and 0.853, respectively.

Machine learning models can accurately predict postoperative 1-year recurrence in patients with HCC, which may help to provide individualized treatment.

While cerebral cavernous malformations (CCMs) have been extensively described, few reports have described the imaging appearance of giant CCMs (GCCMs).

To describe the imaging characteristics of GCCMs and study the reasons for preoperative misdiagnosis.

We retrospectively analyzed the data of 12 patients (5 men, 7 women; mean age, 35.23 ± 12.64 years) with histopathologically confirmed GCCMs. Two radiologists analyzed the CT (n = 12) and MRI (n = 10) features: location, number, size, shape, boundary, signal intensity, and enhancement.

The sellar region, cerebral hemisphere, skull bone, and ventricle were involved in 5, 4, 2, and 1 patients, respectively. Three tumors were irregularly shaped, while nine were oval. Eleven lesions showed slightly high- and/or high-density on CT; 1 lesion appeared as a low-density cyst. Calcifications were found in 11 lesions. Four tumors showed uniform hypointensity on T1-weighted imaging (T1WI) and hyperintense signals on T2-weighted imaging (T2WI). Six tumors showed mixed low-, equal-, and high-intensity signals on T1WI and T2WI. Noticeable contrast enhancement and gradual strengthening were noted on T1WI. Ten lesions showed hemorrhage and hemosiderin deposition. The GCCMs were wrongly diagnosed as cartilage-derived tumors/ meningioma (3 patients); tumor and hematoma (2 patients each); and pituitary tumor/ meningioma, chondroma, chordoma, ependymoma, and macroadenoma (1 patient each).

GCCMs present as an oval mass with slightly high- and/or high-density calcifications on CT and show hemorrhage and hemosiderin accumulation on MRI. Therefore, slightly high- and/or high-density calcification and hemosiderin accumulation are critical clinical characteristics of GCCMs.

Acute inflammation induced by COVID-19 may lead to atherosclerotic plaque development or complicate existing plaque. In this study, we aimed to determine the atherogenic effect of COVID-19 pneumonia, confirmed by thoracic computed tomography, on coronary and carotid arteries in patients who recovered from the disease.

Our study included patients who were diagnosed with COVID-19 in our hospital at least 1 year ago, recovered, and then underwent coronary CT angiography with suspected coronary artery disease. The aim was to evaluate the burden of atherosclerotic plaque in the coronary arteries of these patients who underwent coronary CT angiography.

Patients were assigned to 3 groups according to the results of the CT scan. Group 1 included patients in the control group with no history of COVID-19 (n=36), group 2 included those with mild to moderate pneumonia symptoms (n=43), and group 3 included those with severe pneumonia symptoms (n=29). The calcium scores were 23.25±36.8 in group 1, 27.65±33.4 in group 2, and 53.58±55.1 in group 3. The calcium score was found to be significantly higher in group 3 patients with severe pneumonia (group 1-2 p=0.885, group 1-3 p<0.05, group 2-3 p<0.05).

Although there is no conclusive evidence of a relationship between COVID-19 and atherosclerosis, our study suggests a possible relationship between them. Since this relationship was found especially in cases with severe disease in our study, we believe that the treatment should focus on preventing excessive inflammatory response, and such patients should be under control in terms of coronary artery disease.

This study aimed to explore the similarities and differences in clinical presentations, multidetector computed tomographic (MDCT) features, and treatment of three types of adult intussusceptions based on location.

We retrospectively reviewed 184 adult patients with 192 intussusceptions. Depending on the location, intussusceptions were classified as enteric, ileocolic, and colonic types. The similarities and differences of clinical presentations, MDCT features, and treatment of three types of adult intussusception were compared. Meanwhile, the three types of intussusceptions were further divided into surgical and conservative groups based on the treatment. Uni- and multivariate logistic analyses were used to identify risk factors for intussusception requiring surgery.

Enteric and ileocolic intussusceptions were mainly presented with abdominal pain (78.46% and 85.71%). Hematochezia/melena (64.29%) was the main symptom of colonic intussusception. On MDCT, ileocolic intussusceptions were longer in length and had more signs of intestinal necrosis (hypodense layer, fluid collection and no/poor bowel wall enhancement) than enteric and colonic intussusceptions. Moreover, it was found that 93.88% (46/49) of ileocolic intussusception and 98.59% (70/71) of colonic intussusception belonged to the surgical group, whereas only 43.06% (31/72) of enteric intussusception belonged to the surgical group. Intussusception length (OR=1.171, P=0.028) and discernible lead point on MDCT (OR=21.003, P<0.001) were reliable indicators of enteric intussusception requiring surgery.

Ileocolic intussusception may be more prone to intestinal necrosis than enteric and colonic intussusceptions, requiring more attention from clinicians. Surgery remains the treatment of choice for most ileocolic and colonic intussusceptions. Less than half of enteric intussusceptions require surgery, and MDCT features are effective in identifying them.

We have aimed to investigate the role of ultrasonographic muscle parameters (UMP) in predicting osteosarcopenia in bedridden patients in a palliative care center.

The role of ultrasound has not been evaluated in predicting osteosarcopenia.

Reduced muscle thickness (MT) and cross-sectional area (CSA) have often been observed in individuals with sarcopenia, reflecting muscle loss and atrophy. Meanwhile, the potential role of muscle ultrasound has not been evaluated in predicting osteosarcopenia.

We have conducted a prospective, observational study between January 2021 and 2022. We have recorded the demographics, comorbidities, and nutritional status by using the mini nutritional assessment-short form. We measured handgrip strength with a hand dynamometer and the muscle mass with dual X-ray absorptiometry. Sarcopenia was defined by the European Working Group on Sarcopenia in Older People 2 criteria. Osteoporosis was diagnosed according to the World Health Organization criteria. We have categorized the body phenotypes into four groups: “non-sarcopenic non-osteoporotic,” “sarcopenic alone,” “osteoporotic alone,” and “sarcopenic osteoporotic.” We have measured the subcutaneous fat thickness (SFT), MT, and CSA of the rectus femoris (RF) and biceps brachii (BB) via ultrasonography. A multivariate regression analysis was performed and area under curve (AUC) values were used to evaluate the accuracy of UMPs.

We included 31 patients (mean age: 74.6±12.1 years, 54.8%: male). The prevalences of sarcopenia, osteoporosis, and sarcopenic osteoporosis were 71%, 48.4%, and 41.9%, respectively. Only the “sarcopenic osteoporotic” phenotype was negatively correlated with all UMPs. In the regression analysis, only the “sarcopenic osteoporotic” phenotype was independently associated with RFCSA (ß=-0.456, p= 0.024). The AUC for all patients was >0.700.

RFCSA measurement might be useful in the screening for osteosarcopenia. This has been the first study investigating the relationship between UMPs and body phenotypes. Multi-center and large-scale studies are, however, needed.

This study aimed to investigate whether there is a correlation between quantitative parameters of dual-energy computed tomography (DECT) and the relative expression of HIF-1α in patients with non-small cell lung cancer (NSCLC) to preliminarily explore the value of DECT in evaluating the hypoxia of tumor microenvironment and tumor biological behavior and provide more information for the treatment of NSCLC.

This retrospective research included 36 patients with pathologically confirmed NSCLC who underwent dual-energy enhanced CT scans. The quantitative parameters of DECT were analyzed, including iodine concentration, water concentration, the CT values corresponding to 40keV, 70keV, 100keV, and 130keV in arterial and venous phases, and the normalized iodine concentration and the slope of the energy spectrum curve were calculated. Postoperative specimens underwent HIF immunohistochemical staining by two pathologists. Spearman correlation analysis was adopted as the statistical methodology. The data were analyzed by SPSS26.0 statistical software.

Water concentration (r=0.659, P<0.001 and r= 0.632, P<0.001, the CT values corresponding to 100keV (r=0.645, P<0.001 and r= 0.566, P<0.001) and 130keV (r=0.687, P<0.001 and r= 0.682, P<0.001) in arterial and venous phases, and CT value of 70keV in arterial phase (r=0.457, P=0.005) were positively correlated with HIF-1α expression level. There was no correlation among iodine concentration, standardized iodine concentration, CT value of 40keV, λHU, and HIF-1α expression in arterial and venous levels (P >0.05).

The quantitative parameters of DECT have a certain correlation with HIF-1α expression in NSCLC. Moreover, it has been demonstrated that DECT can be used to predict hypoxia in tumor tissues and the prognosis of lung cancer patients.

Unilateral absence of the pulmonary artery (UAPA) is a very rare congenital anomaly.

To analyze the diagnostic strategy applied to seven patients with UAPA who were examined and subsequently treated at the National Lung Hospital, Hanoi, Vietnam.

All seven patients, including three pediatric cases (1, 2, and 14 years old) and four adult cases (21, 26, 44, and 53 years old), had a history of recurrent pneumonia, and the clinical symptoms on admission included cough, progressive dyspnea, chest pain, and fatigue. The patients were initially examined clinically, followed by hematological testing, blood biochemistry testing, and chest X-ray radiology. The results suggested UAPA, so echocardiography and contrast-enhanced chest computed tomography (CT) were performed as soon as practical.

The echocardiographic and CT imaging findings confirmed the suspected diagnosis of UAPA in all seven patients, which was accompanied by congenital heart disease in three patients. Three of the seven patients had mild and medium pulmonary hypertension. All seven patients were treated with drugs, which led to improvement in symptoms.

Frontal chest X-ray provided the initial signs suggesting a diagnosis of UAPA. Subsequent echocardiography and contrast-enhanced chest CT were effective diagnostic tools for fast and accurate confirmation of UAPA.

To explore the application value of a combined model based on multi-parameter MRI radiomics and clinical features in preoperative prediction of lymphatic vascular space invasion (LVSI) in endometrial carcinoma (EC).

This retrospective study collected the clinicopathological and imaging data of 218 patients with EC in Yuncheng Central Hospital from March 2018 to May 2022. The patients were randomly divided into training group (n=152) and validation group (n= 66) according to the ratio of 7: 3. Based on the ADC, CE-sag, CE-tra, DWI, T2WI-sag-fs, T2WI-tra sequence images of each patient, the region of interest was manually segmented and the features were extracted. The four-step dimensionality reduction method based on max-relevance and min-redundancy (MRMR) and least absolute shrinkage and selection operator (LASSO) regression was used for feature selection and radiomics model construction. Independent predictors of clinicopathological features were screened by multivariate logistic regression analysis. The imaging model based on ADC, CE-sag, CE-tra, DWI, T2WI-sag-fs, T2WI-tra single sequence and combined sequence and the fusion model with clinicopathological features were constructed, and the nomogram was made. ROC curve, correction curve and decision analysis curve were used to evaluate the efficacy and clinical benefits of the nomogram.

There was no significant difference in general clinical data between the training and validation groups (P > 0.05). After screening the extracted features, 16 radiomics features were obtained, which were all related to LVSI in EC patients (P < 0.05). The area under the ROC curve (AUC) of the six independent sequence radiomics models in the training group was 0.807, 0.794, 0.826, 0.794, 0.828, 0.824, respectively. The AUC corresponding to the radiomics model constructed by the combined sequence was 0.884, and the diagnostic efficiency was the best, which was verified in the validation group. The AUC of the nomogram constructed by the combined radiomics model and age、maximum tumor diameter(MTD), lymph node enlargement (LNE) in the training group and the validation group were 0.914 and 0.912, respectively. The correction curve shows that the nomogram has good correction performance. The decision curve suggests that taking radiomics nomogram to predict LVSI net benefit when the risk threshold is > 10% is better than considering all patients as LVSI+ or LVSI-.

The combined model based on multi-parametric MRI radiomics features and clinical features has good predictive value for LVSI status in EC patients.

Nowadays, people attach increasing importance to accurate and timely disease diagnosis and personalized treatment. Because of the uncertainty and latency of the pathogenesis, it is difficult to detect breast tumour early. With higher resolution, magnetic resonance imaging (MRI) has become an important method for early detection of cancer in recent years. At present, DL technology can automatically study imaging features of different depths.

This work aimed to use DL to study medical image-assisted diagnosis.

The image data were collected from the patients. ROI (region of interest) containing the complete tumor area in the medical image was generated. The ROI image was extracted, and the extracted feature data were expanded. By constructing a three-dimensional (3D) CNN model, the evaluation indicators of breast tumour diagnosis results have been proposed. In the experiment part, 3D CNN model and other models have been used to diagnose the medical image of breast tumour.

The 3D CNN model exhibited good ROI region extraction effect and breast tumor image diagnosis effect, and the average diagnostic accuracy of breast tumor image diagnosis was 0.736, which has been found to be much higher than other models and could be applied to breast tumor medical image-aided diagnosis.

The 3D CNN model has been trained by combining the two-dimensional CNN training mode, and the evaluation index of diagnostic results has been established. The experimental part verified the medical image diagnosis effect of the 3D CNN model. The model had exhibited a high ROI region extraction effect and breast tumor image diagnosis effect.

In the last few years, coronavirus disease 2019 (COVID-19) has changed human lifestyle, behavior, and perception of life. This disease is caused by severe acute respiratory syndrome coronavirus 2 (SARS-COV-2). In the literature, there are limited studies about the late renal effects of COVID-19 that reflect the systemic involvement of this disease.

In the present study, we aimed to compare sonoelastographic changes in both kidneys between patients who had totally recovered from COVID-19 and healthy individuals using strain wave elastography (SWE).

This study was conducted between June 2021 and May 2022 in Kahramanmaraş City Hospital Department of Radiology. File and archive records were retrospectively evaluated. Basic demographic, laboratory, and renal ultrasonography (USG) and sonoelastographic findings were screened and noted. Two groups were defined to compare sonoelastographic findings. Post-/long-COVID-19 group had 92 post-long COVID-19 patients, and the comparator group comprised healthy individuals”. Both groups’ demographic, laboratory, and ultrasound-elastographic findings were assessed.

The post-long COVID-19 group had a higher renal elastographic value than the comparator group (1.52 [0.77–2.3] vs. 0.96 [0.54–1.54], p<0.001). There were no statistically significant differences between the two groups in terms of age (p=0.063), gender (p=0.654), or body mass index (BMI) (p=0.725), however, there was a significant difference observed between the two groups in the renal strain ratio (RSR). According to an receiver operating characteristic curve (ROC) analysis, an RSR cutoff of >1.66 predicted post-long COVID-19 with 44.9% sensitivity and 81.9% specificity. (AUC=0.655, p<0.001). A separate ROC analysis was performed to predict post-long COVID-19 with a BMI cutoff of <33.52, kg/m² sensitivity of 92.4% and specificity of 17% (AUC=0.655, p<0.001).

We demonstrated that renal parenchymal stiffness increases with SWE in post-long COVID-19 patients.

In this work, we have used histopathology as the gold standard for the diagnosis, calculated the sensitivity and positive predictive value (PPV) of computed tomography (CT), and analyzed the CT and clinical characteristics of pathologically proven elastofibromas.

A systematic retrospective analysis was performed on all patients with infrascapular lesions who were treated in the hospital from 2006 to 2018. CT and histopathological examinations were performed for all cases, and the CT sensitivity and PPV for the diagnosis of elastofibroma were calculated. 12 of 53 cases (20 lesions) underwent enhanced CT scan after CT plain scan, and the related clinical and CT features of elastofibromas have been discussed.

Of the 54 patients treated during the study, CT diagnosis was consistent with histopathology in 53 cases. One was a false-positive patient. The PPV and sensitivity of the CT in the diagnosis of elastofibroma were 93.3% (95% CI 68.0%-99.8%) and 100%, respectively. The CT values of 12 patients with 20 lesions on plain and enhanced scans were statistically significant (P=0.001). The prevalence of elastofibromas in males and females was statistically significant (P=.000). There was no statistically significant difference in the incidence of left and right elastofibromas (P=0.752). There was no significant difference in the volume of left and right lesions (P=0.209) and the volume of elastofibromas between males and females (P=.474).

CT is the most practical tool for the evaluation of elastofibromas in the infrascapular region.

In late December 2019, Wuhan, the capital of Hubei Province, China, became the center of an outbreak of pneumonia caused by severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2).

The radiological changes in the lungs of critical people with coronavirus disease 2019 (COVID-19) pneumonia at different times have not been fully characterized. We aim to describe the computed tomography findings of patients with critical COVID-19 pneumonia at different disease stages.

Clinical and laboratory features of critical patients were assessed. CT scans were assigned to groups 1, 2, 3, or 4 based on the interval from symptom onset (within 2 weeks; ≥ 2-4 weeks; ≥ 4-6 weeks; or ≥ 6 weeks, respectively). Imaging features were analyzed and compared across the four groups. Total CT scores, corresponding periods of laboratory findings, and glucocorticoid dosages during the imaging intervals were longitudinally observed in five patients with complete data.

All 11 critical patients (median age: 60 years [42-69]) underwent a total of 40 CT examinations, and the acquisition times ranged from 1-59 days after symptom onset. Median total CT scores were 18 (9-25.25); 445 (42.88-47.62); 4375 (38.62-49.38); and 42 (32.25-53.25) in groups 1, 2, 3, and 4, respectively. The observed lesions were mainly bilateral (37 [92.5%]). The median values of involved lung segments were 10.5 (4.5-13.5); 17 (16-18.5); 18 (18-19.5); and 18 (18-19) in groups 1-4, respectively. The predominant patterns of observed abnormalities were ground-glass opacities (GGO) (9 [90%]); GGO with reticulation and mixed patterns (3 [37.5%] for both); GGO with consolidation (3 [30%]); and GGO with reticulation (8 [66.7%]) in groups 1-4, respectively. Patients developed fibrotic manifestations at later stages.

Critical patients with COVID-19 infection generally presented with temporally changing abnormal CT features from focal unilateral to diffuse bilateral GGO and consolidation that progressed to or coexisted with reticulation in the long term after symptom onset. Low-dose glucocorticoids may be effective in patients with interstitial changes on CT findings.

Neurolymphomatosis (NL) is a rare disease. Ultrasound (US) plays a crucial role in diagnosing and following up the NL.

A 59-year-old man was hospitalized with acute pain in the left upper extremity. Ultrasound revealed segmental swelling of multiple nerves around his left elbow with abundant blood flow signals. Contrast-Enhanced Ultrasound (CEUS) showed a rapid, complete and homogenous enhancement in the nerve lesions in the early arterial phase. The NL was confirmed by imaging and flow cytometry, and he accepted chemotherapy. The post-therapeutic ultrasound showed that the nerves in the left upper limb were basically normal. Unfortunately, the patient died of cerebral metastasis in 5 months.

The nerve US and CEUS can show specific manifestations and provide more diagnostic information about NL.