Current Medical Imaging - Volume 20, Issue 1, 2024

This study aimed to predict liver injury in AP patients by establishing a radiomics model based on CECT.

A total of 1223 radiomic features were extracted from late arterial-phase pancreatic CECT images of 209 AP patients (146 in the training cohort and 63 in the test cohort), and the optimal radiomic features retained after dimensionality reduction by LASSO were used to construct a radiomic model through logistic regression analysis. In addition, clinical features were collected to develop a clinical model, and a joint model was established by combining the best radiomic features and clinical features to evaluate the practicality and application value of the radiomic models, clinical model, and combined model.

Four potential features were selected from the pancreatic parenchyma to construct the radiomic model, and the AUC of the radiomic model was significantly greater than that of the clinical model for both the training cohort (0.993 vs. 0.653, p = 0.000) and test cohort (0.910 vs. 0.574, p = 0.000). The joint model had a greater AUC than the radiomics model for both the training cohort (0.997 vs. 0.993, p = 0.357) and the test cohort (0.925 vs. 0.910, p = 0.302).

The radiomic model based on CECT has good performance in predicting liver injury in AP patients and can guide clinical decision-making and improve the prognosis of patients with AP.

The primary objective of this study was to conduct a comparative analysis of the diagnostic efficacy of [¹⁸F]-FDG PET/CT and [¹⁸F]-FDG PET/MRI in the detection of breast cancer lymph node metastasis.

We conducted a comprehensive search on PubMed, Embase, and Web of Science databases, encompassing eligible articles until March 2023. The pooled sensitivity and specificity for [¹⁸F]-FDG PET/CT and [¹⁸F]-FDG PET/MRI have been reported as estimates with 95% Confidence Intervals (CIs) using a bivariate random-effect model. Utilizing the I square (I²) statistic, heterogeneity among pooled studies was evaluated. The quality assessment of the included studies was conducted using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) methodology.

We included 18 studies (2057 patients). The sensitivity, specificity, and AUC (Area Under the Curve) values of [¹⁸F]-FDG PET/CT for overall lymph node metastasis in breast cancer have been found to be 0.58 (0.39 - 0.75), 0.83 (0.69-0.92), and 0.79 (0.75-0.82), respectively. Correspondingly, the values for [¹⁸F]-FDG PET/MRI were found to be 0.76 (0.60-0.88), 0.85 (0.77-0.91), and 0.89 (0.86-0.91), respectively. The sensitivity, specificity, and AUC values of [¹⁸F]-FDG PET/CT for axillary lymph node metastasis in breast cancer were 0.52 (0.37-0.67), 0.84 (0.68-0.92), and 0.73 (0.69-0.76), respectively. Correspondingly, the values for [¹⁸F]-FDG PET/MRI were 0.84 (0.76-0.89), 0.87 (0.75-0.94), and 0.86 (0.83-0.89), respectively.

This study has suggested [¹⁸F]-FDG PET/MRI to have greater diagnostic power than [¹⁸F]-FDG PET/CT in detecting lymph node metastasis in breast cancer. However, the [¹⁸F]-FDG PET/MRI results have been obtained from a small sample size study, and more and larger prospective studies are needed for further confirmation on this issue.

Early detection of pulmonary nodules is critical for the clinical diagnosis and management of pulmonary nodules. Computed tomography imaging is currently the best imaging method for detecting pulmonary nodules.

This study proposes and applies a new thresholding-based method for identifying pulmonary nodules in computed tomography images.

The proposed method involves segmenting the lung volume and identifying candidate nodules based on their intensity levels, which are higher than those of the lung parenchyma. Reference points on the histogram curve are used to determine a threshold value, and filtering by geometric characteristics is applied to reduce false positives. The performance of the proposed method is evaluated on a training set consisting of 35 nodules distributed among 16 cases with ground truth using the SPIE-AAPM Lung CT Challenge Database and ELCAP Public Lung Image Database.

The proposed method shows a significant reduction in false positives, filtering from an average of 12,380 candidate nodules to 19 detected nodules. The method also demonstrates a sensitivity of 88.6% for detecting pulmonary nodules with an error of 1 nodule in cases where complete detection is not reached.

The proposed thresholding-based method improves the sensitivity of identifying pulmonary nodules in computed tomography images while reducing false positives.

Accurate identification of vascular lumen region founded the base of bubble detection and bubble grading, which played a significant role in the detection of vascular gas emboli for the diagnosis of decompression sickness.

To assist in the detection of vascular bubbles, it is crucial to develop an automatic algorithm that could identify vascular lumen areas in ultrasound videos with the interference of bubble presence.

This article proposed an automated vascular lumen region recognition (VLRR) algorithm that could sketch the accurate boundary between vessel lumen and tissues from dynamic 2D ultrasound videos. It adopts 2D ultrasound videos of the lumen area as input and outputs the frames with circled vascular lumen boundary of the videos. Normalized cross-correlation method, distance transform technique, and region growing technique were adopted in this algorithm.

A double-blind test was carried out to test the recognition accuracy of the algorithm on 180 samples in the images of 6 different grades of bubble videos, during which, intersection over union and pixel accuracy were adopted as evaluation metrics. The average IOU on the images of different bubble grades reached 0.76. The mean PA on 6 of the images of bubble grades reached 0.82.

It is concluded that the proposed method could identify the vascular lumen with high accuracy, potentially applicable to assist clinicians in the measurement of the severity of vascular gas emboli in clinics.

The second highest cause of death among males is Prostate Cancer (PCa) in America. Over the globe, it’s the usual case in men, and the annual PCa ratio is very surprising. Identical to other prognosis and diagnostic medical systems, deep learning-based automated recognition and detection systems (i.e., Computer Aided Detection (CAD) systems) have gained enormous attention in PCA.

These paradigms have attained promising results with a high segmentation, detection, and classification accuracy ratio. Numerous researchers claimed efficient results from deep learning-based approaches compared to other ordinary systems that utilized pathological samples.

This research is intended to perform prostate segmentation using transfer learning-based Mask R-CNN, which is consequently helpful in prostate cancer detection.

Lastly, limitations in current work, research findings, and prospects have been discussed.

This research investigates the impact of pruning on reducing the computational complexity of a five-layered Convolutional Neural Network (CNN) designed for classifying MRI brain tumors. The study focuses on enhancing the efficiency of the model by removing less important weights and neurons through pruning.

This research aims to analyze the impact of pruning on the computational complexity of a CNN for MRI brain tumor classification, identifying optimal pruning percentages to balance reduced complexity with acceptable classification performance.

The proposed CNN model is implemented for the classification of MRI brain tumors. To reduce time complexity, weights and neurons of the trained model are pruned systematically, ranging from 0 to 99 percent. The corresponding accuracies for each pruning percentage are recorded to assess the trade-off between model complexity and classification performance.

The analysis reveals that the model's weights can be pruned up to 70 percent while maintaining acceptable accuracy. Similarly, neurons in the model can be pruned up to 10 percent without significantly compromising accuracy.

This research highlights the successful application of pruning techniques to reduce the computational complexity of a CNN model for MRI brain tumor classification. The findings suggest that judicious pruning of weights and neurons can lead to a significant improvement in inference time without compromising accuracy.

Deep learning models have recently been preferred to perform certain image-processing tasks. Recently, with the increasing radiation, heat, and poor lighting conditions, the raw image samples may contain noisy and ambiguous information.

To process these images, the deep learning model requires a large number of data samples to learn the missing information from other clear data samples. This necessitates training the neural network with a huge dataset.

The researchers are now attempting to filter and improve such noisy images via preprocessing in order to provide valid and accurate feature information to the neural network layers. However, certain research studies claim that some useful information may be lost when the image is not preprocessed with an appropriate filter or enhancement technique. The MSA (meta-synthesis and analysis) approach is utilized in this work to present the impact of the image processing applications done with and without preprocessing steps. Also, this work summarizes the existing deep learning-based image processing models utilizing or not preprocessing steps in their implementation.

This work has also found that 85% of the existing techniques involve a preprocessing step while developing a deep learning model. However, a maximum accuracy of 96.89% is observed on Sine-Net when it is implemented without a preprocessing and the same model gave 96.85% when implemented with preprocessing.

This research provides various research insights on the requirement and non-requirement of preprocessing steps in a deep learning-based implementation.

Hyperostosis frontalis interna (HFI) is a common and often incidental finding seen on imaging. There is a significant paucity of radiology literature, particularly regarding the MRI imaging appearance of HFI.

We reported two cases of HFI on MRI, which showed focal enhancement. These were stable on long-term follow-up studies and thought to be most consistent with benign enhancement.

Further studies are needed to elucidate the underlying pathogenesis; however, it is important to be aware that regions of HFI may demonstrate variable enhancement and are sometimes mistaken for osseous metastatic disease.

To investigate the feasibility of constructing new geometric parameters that correlate well with dosimetric parameters.

100 rectal cancer patients were enrolled. The targets were identified manually, while the organs at risk (bladder, small bowel, left and right femoral heads) were segmented both manually and automatically. The radiotherapy plans were optimized according to the automatically contoured organs at risk. Forty cases were randomly selected to establish the relationship between dose and distance for each organ at risk, termed “dose-distance curves,” which were then applied to the new geometric parameters. The correlation between these new geometric parameters and dosimetric parameters was analyzed in the remaining 60 test cases.

The “dose-distance curves” were similar across the four organs at risk, exhibiting an inverse function shape with a rapid decrease initially and a slower rate at a later stage. The Pearson correlation coefficients of new geometric parameters and dosimetric parameters in the bladder, small intestine, and left and right femur heads were 0.96, 0.97, 0.88, and 0.70, respectively.

The new geometric parameters predicated on “distance from the target” showed a high correlation with corresponding dosimetric parameters in rectal cancer cases. It is feasible to utilize the new geometric parameters to evaluate the dose deviation attributable to automatic segmentation.

Patient safety is paramount in ultrasound procedures, particularly in obstetric ultrasounds involving both the mother and fetus. The thermal and mechanical indices (TI and MI) serve as crucial indicators of the acoustic output during ultrasound. Clinicians and specialists must know these indices and ensure they are within safe ranges. This study aimed to assess the parameters of acoustic output power employed in obstetric ultrasound (thermal and mechanical index).

A cross-sectional observational study conducted at Maternity and Children's Hospital in Al-Madinah Al-Munawwarah, the data was collected from obstetric scanning of 411 pregnant females using a data collection sheet including gravida and women's age, gestational age, scan mode, scan time, and thermal and mechanical index (TI and MI) values.

The study found that there were significant differences in safety indices measurement between different modes; in Pulsed Doppler, mean Thermal Index Bone (TIb) had the highest value (1.60±0.40), and the Mechanical Index (MI) was the lowest (0.68±0.33). There were insignificant differences in safety indices values in different modes in different trimesters. The thermal indices of soft tissue and bony structure (TIs and TIb) of brightness mode (B-mode) were constant in all trimesters, but the MI in the first trimester was lower than in the other trimesters.

This study concluded that the mean values of thermal indices used in B mode , M mode and Color Doppler lie within the Recommended limit of (BMUS) British Medical Ultrasound Society (below 0.7) except for Pulsed Doppler it was exceed 1.5. While for MI in different ultrasound modes except in pulsed Doppler the average values is higher than 0.7 which was recommended by (BMUS) British Medical Ultrasound Society and lower than 1.9 which was the maximum threshold approved by FDA (Food and drug Administration). The average scanning time is low (6.4 minute) reflect the safe use of ultrasound in obstetrics in this study.

Utilizing ultrasound radiomics, we developed a machine learning (ML) model to construct a nomogram for the non-invasive evaluation of glomerular status in diabetic kidney disease (DKD).

Patients with DKD who underwent renal biopsy were retrospectively enrolled between February 2017 and February 2023. The patients were classified into mild or moderate-severe glomerular severity based on pathological findings. All patients were randomly divided into a training (n = 79) or testing cohort (n = 35). Radiomics features were extracted from ultrasound images, and a logistic regression ML algorithm was applied to construct an ultrasound radiomic model after selecting the most significant features using univariate analysis and the least absolute shrinkage and selection operator algorithm (LASSO). A clinical model was created following univariate and multivariate logistic regression analyses of the patient's clinical characteristics. Then, the clinical-radiomics model was constructed by combining rad scores and independent clinical characteristics and plotting the nomogram. The receiver operating characteristic curve (ROC) and decision curve analysis (DCA), respectively, were used to evaluate the prediction abilities of the clinical model, ultrasound-radiomics model, and clinical-radiomics model.

A total of 114 DKD patients were included in the study, including 43 with mild glomerulopathy and 71 with moderate-severe glomerulopathy. The area under the curve (AUC) for the clinical model based on clinical features and the radiomic model based on 2D ultrasound images in the testing cohort was 0.729 and 0.761, respectively. Further, the AUC for the clinical-radiomics nomogram was constructed by combining clinical features, and the rad score was 0.850 in the testing cohort. The outcomes were better than those of both the radiomic and clinical single-model approaches.

The nomogram constructed by combining ultrasound radiomics and clinical features has good performance in assessing the glomerular status of patients with DKD and will help clinicians monitor the progression of DKD.

This study aimed to evaluate the Pharmacovigilance (PV) and severity of hypersensitivity reactions induced by non-ionic Iodinated Contrast Media (ICM) in the radiology diagnosis reported to the United States Food and Drug Administration Adverse Event Reporting System (FAERS).

We retrospectively reviewed the reports of ICM-induced hypersensitivity reactions submitted to the FAERS database between January 2015 and January 2023 and conducted a disproportionality analysis. The seven most common non-ionic ICM, including iohexol, iopamidol, ioversol, iopromide, iomeprol, iobitridol, and iodixanol, were chiefly analyzed. Our primary endpoint was the PV of non-ionic ICM-induced total hypersensitivity events. STATA 17.0 MP was used for statistical analysis.

In total, 35357 reports of adverse reaction events in radiology diagnosis were retrieved from the FAERS database. Among them, 6181 reports were on hypersensitivity reaction events (mean age: 57.1 ± 17.8 years). The hypersensitivity reaction-related PV signal was detected for iohexol, ioversol, iopromide, iomeprol, iobitridol, and iodixanol, but not for iopamidol. The proportion of iomeprol-induced hypersensitivity reactions and the probability of ioversol-induced severe hypersensitivity reactions have been found to be significantly increased.

The probability and severity of hypersensitivity reaction events in non-ionic ICM are different. Iohexol, ioversol, iopromide, iomeprol, iobitridol, and iodixanol have higher risks compared to iopamidol. In addition, the constituent ratio of hypersensitivity reactions induced by iomeprol is significantly increased, and the associated probability induced by ioversol is significantly increased.

The increasing longevity of the population has made Alzheimer's disease (AD) a significant public health concern. However, the challenge of accurately distinguishing different disease stages due to limited variability within the same stage and the potential for errors in manual classification highlights the need for more precise approaches to classifying AD stages. In the field of deep learning, the ResNet50V2 model stands as a testament to its exceptional capabilities in image classification tasks.

The dataset employed in this study was sourced from Kaggle and consisted of 6400 MRI images that were meticulously collected and rigorously verified to assure their precision. The selection of images was conducted with great attention to detail, drawing from a diverse array of sources.

This study focuses on harnessing the potential of this model for AD classification, a task that relies on extracting disease-specific features. Furthermore, to achieve this, a multi-class classification methodology is employed, using transfer learning and fine-tuning of layers to adapt the pre-trained ResNet50V2 model for AD classification. Notably, the impact of various input layer sizes on model performance is investigated, meticulously striking a balance between capacity and computational efficiency. The optimal fine-tuning strategy is determined by counting layers within convolution blocks and selectively unfreezing and training individual layers after a designated layer index, ensuring consistency and reproducibility. Custom classification layers, dynamic learning rate reduction, and extensive visualization techniques are incorporated.

The model's performance is evaluated using accuracy, AUC, precision, recall, F1-score, and ROC curves. The comprehensive analysis reveals the model's ability to discriminate between AD stages. Visualization through confusion matrices aided in understanding model behavior. The rounded predicted labels enhanced practical utility.

This approach combined empirical research and iterative refinement, resulting in enhanced accuracy and reliability in AD classification. Our model holds promise for real-world applications, achieving an accuracy of 96.18%, showcasing the potential of deep learning in addressing complex medical challenges.

Accurate segmentation of liver tumor regions in medical images is of great significance for clinical diagnosis and the planning of surgical treatments. Recent advancements in machine learning have shown that convolutional neural networks are powerful in such image processing while largely reducing human labor. However, the variable shape, fuzzy boundary, and discontinuous tumor region of liver tumors in medical images bring great challenges to accurate segmentation. The feature extraction capability of a neural network can be improved by expanding its architecture, but it inevitably demands more computing resources in training and hyperparameter tuning.

This study presents a Dynamic Context Encoder Network (DCE-Net), which incorporates multiple new modules, such as the Involution Layer, Dynamic Residual Module, Context Extraction Module, and Channel Attention Gates, for feature extraction and enhancement.

In the experiment, we used a liver tumor CT dataset of LiTS2017 to train and test the DCE-Net for liver tumor segmentation. The experimental results showed that the four evaluation indexes of the method, precision, recall, dice, and AUC, were 0.8961, 0.9711, 0.9270, and 0.9875, respectively. Furthermore, our ablation study reported that the accuracy and training efficiency of our network were markedly superior to the networks without involution or dynamic residual modules.

Therefore, the DCE-Net proposed in this study has great potential for automatic segmentation of liver lesion tumors in the clinical diagnostic environment.

Patients with diffuse large B-cell lymphoma (DLBCL) often experience a poor prognosis due to cardiac damage induced by anthracycline chemotherapy, with left ventricular diastolic dysfunction manifesting early. Vector Flow Mapping (VFM) is a novel technology, and its effectiveness in detecting left ventricular diastolic dysfunction following anthracycline chemotherapy remains unverified.

This study evaluates left ventricular diastolic function in DLBCL patients after anthracycline chemotherapy using vector flow mapping (VFM).

We prospectively enrolled 54 DLBCL patients who had undergone anthracycline chemotherapy (receiving a minimum of 4 cycles) as the case group and 54 age- and sex-matched individuals as controls. VFM assessments were conducted in the case group pre-chemotherapy (T0), post-4 chemotherapy cycles (T4), and in the control group. Measurements included basal, middle, and apical segment energy loss (ELb, ELm, ELa) and intraventricular pressure differences (IVPDb, IVPDm, IVPDa) across four diastolic phases: isovolumic relaxation (D1), rapid filling (D2), slow filling (D3), and atrial contraction (D4).

When comparing parameters between the control and case groups at T0, no significant differences were observed in general data, conventional ultrasound parameters, and VFM parameters (all P > 0.05). From T0 to T4, ELa significantly increased throughout the diastole cycle (all P < 0.05); ELm increased only during D4 (all P < 0.05); and ELb increased during D1, D2, and D4 (all P < 0.05). All IVPD measurements (IVPDa, IVPDm, IVPDb) increased during D1 and D4 (all P < 0.05) but decreased during D2 and D3 (all P < 0.05). Significant positive correlations were identified between ELa-D4, IVPDa-D4, and parameters A, e’, E/e,’ and LAVI (all r > 0.5, all P < 0.001). Negative correlations were noted with E/A for ELa-D4 IVPDa-D4 (all r < -0.5, all P < 0.001). Positive correlations were observed for IVPDa-D1, IVPDa-D2 with E, E/e’, and LAVI (0.3

VFM parameters demonstrate a certain correlation with conventional diastolic function parameters and show promise in assessing left ventricular diastolic function. Furthermore, VFM parameters exhibit greater sensitivity to early diastolic function changes, suggesting that VFM could be a novel method for evaluating differences in left ventricular diastolic function in DLBCL patients before and after chemotherapy.

This study aimed to investigate the impact of puerarin early intervention on growth parameters and Hepatic Fat Signal Fraction (HFF) quantification in Intrauterine Growth Restricted (IUGR) rats through Proton Magnetic resonance spectroscopy (¹H-MRS).

Pregnant rats were divided into three groups: control, IUGR with puerarin treatment, and IUGR without treatment. The treatment and non-treatment groups were received a low-protein diet during pregnancy, while the control group received a normal diet. After birth, pups in the treatment group received a unilateral intraperitoneal injection of 50 mg/kg/d puerarin. Male rats were evaluated at 3,8 and 12 weeks, including measurements of weight, body length and waist circumference and body mass index (BMI). Conventional magnetic resonance imaging and ¹H-MRS were conducted using a 3.0 T whole-body MR scanner.

Newborn pups in the treatment and non-treatment groups showed significantly lower body weight, BMI, and body length at 3 weeks compared to the control group. However, there were no significant differences in HFF and waist circumference between the three groups at 3 weeks. At 8 and 12 weeks post-delivery, significant differences in body weight, BMI, waist circumference were observed in newborn pups of IUGR non-treatment rats compared to the control group. In contrast, there were no significant differences in body weight, BMI, waist circumference between the treatment group and the control group at 8 and 12 weeks. Moreover, the treatment group exhibited notably higher HFF compared to the control group at both time points. At 12 weeks post-birth, a significant difference in HFF was observed between the IUGR non-treatment and treatment groups, although no significant difference was found at 8 weeks.

Early intervention with puerarin following birth has a significant impact on liver fat content and may potentially reduce adult obesity among IUGR rats.

Transorbital Ultrasonography (TOS) is a promising imaging technology that can be used to characterize the structures of the optic nerve and the potential alterations that may occur in those structures as a result of an increase in intracranial pressure (ICP) or the presence of other disorders such as multiple sclerosis (MS) and hydrocephalus.

In this paper, the primary objective is to develop a fully automated system that is capable of segmenting and calculating the diameters of structures that are associated with the optic nerve in TOS images. These structures include the optic nerve diameter sheath (ONSD) and the optic nerve diameter (OND).

A fully convolutional neural network (FCN) model that has been pre-trained serves as the foundation for the segmentation method. The method that was developed was utilized to collect 464 different photographs from 110 different people, and it was accomplished with the assistance of four distinct pieces of apparatus.

An examination was carried out to compare the outcomes of the automatic measurements with those of a manual operator. Both OND and ONSD have a typical inaccuracy of -0.12 0.32 mm and 0.14 0.58 mm, respectively, when compared to the operator. The Pearson correlation coefficient (PCC) for OND is 0.71, while the coefficient for ONSD is 0.64, showing that there is a positive link between the two measuring tools.

A conclusion may be drawn that the technique that was developed is automatic, and the average error (AE) that was reached for the ONSD measurement is compatible with the ranges of inter-operator variability that have been discovered in the literature.

This study introduces SkinLiTE, a lightweight supervised contrastive learning model tailored to enhance the detection and typification of skin lesions in dermoscopic images. The core of SkinLiTE lies in its unique integration of supervised and contrastive learning approaches, which leverages labeled data to learn generalizable representations. This approach is particularly adept at handling the challenge of complexities and imbalances inherent in skin lesion datasets.

The methodology encompasses a two-phase learning process. In the first phase, SkinLiTE utilizes an encoder network and a projection head to transform and project dermoscopic images into a feature space where contrastive loss is applied, focusing on minimizing intra-class variations while maximizing inter-class differences. The second phase freezes the encoder's weights, leveraging the learned representations for classification through a series of dense and dropout layers. The model was evaluated using three datasets from Skin Cancer ISIC 2019-2020, covering a wide range of skin conditions.

SkinLiTE demonstrated superior performance across various metrics, including accuracy, AUC, and F1 scores, particularly when compared with traditional supervised learning models. Notably, SkinLiTE achieved an accuracy of 0.9087 using AugMix augmentation for binary classification of skin lesions. It also showed comparable results with the state-of-the-art approaches of ISIC challenge without relying on external data, underscoring its efficacy and efficiency. The results highlight the potential of SkinLiTE as a significant step forward in the field of dermatological AI, offering a robust, efficient, and accurate tool for skin lesion detection and classification. Its lightweight architecture and ability to handle imbalanced datasets make it particularly suited for integration into Internet of Medical Things environments, paving the way for enhanced remote patient monitoring and diagnostic capabilities.

This research contributes to the evolving landscape of AI in healthcare, demonstrating the impact of innovative learning methodologies in medical image analysis.

Radiomics can quantify pulmonary nodule characteristics non-invasively by applying advanced imaging feature algorithms. Radiomic textural features derived from Computed Tomography (CT) imaging are broadly used to predict benign and malignant pulmonary nodules. However, few studies have reported on the radiomics-based identification of nodular Pulmonary Cryptococcosis (PC).

This study aimed to evaluate the diagnostic and differential diagnostic value of radiomic features extracted from CT images for nodular PC.

This retrospective analysis included 44 patients with PC (29 males, 15 females), 58 with Tuberculosis (TB) (39 males, 19 females), and 60 with Lung Cancer (LC) (20 males, 40 females) confirmed pathologically. Models 1 (PC vs. non-PC), 2 (PC vs. TB), and 3 (PC vs. LC) were established using radiomic features. Models 4 (PC vs. TB) and 5 (PC vs. LC) were established based on radiomic and CT features.

Five radiomic features were predictive of PC vs. non-PC model, but accuracy and Area Under the Curve (AUC) were 0.49 and 0.472, respectively. In model 2 (PC vs. TB) involving six radiomic features, the accuracy and AUC were 0.80 and 0.815, respectively. Model 3 (PC vs. LC) with six radiomic features performed well, with AUC=0.806 and an accuracy of 0.76. Between the PC and TB groups, model 4 combining radiomics, distribution, and PI, showed AUC=0.870. In differentiating PC from LC, the combination of radiomics, distribution, PI, and RBNAV achieved AUC=0.926 and an accuracy of 0.90.

The prediction models based on radiomic features from CT images performed well in discriminating PC from TB and LC. The individualized prediction models combining radiomic and CT features achieved the best diagnostic performance.

Although clinical assessment has historically been the primary method used for diagnosing and staging pediatric localized scleroderma (LS), high-frequency ultrasonography (HFUS) is being investigated as a more accurate method for evaluating lesions.

This study aimed to assess, compare dermal and subcutaneous tissue characteristics and enhance lesion staging in pediatric LS patients using HFUS.

Twenty two LS patients were cross-sectionally evaluated with B-mode ultrasonography. Lesions were clinically staged, and dermal and subcutaneous tissue characteristics were compared with healthy tissue using HFUS.

Among 55 lesions, 27 were active/new (49.1%), and 28 were atrophic/old (50.9%). Active lesions typically had increased dermal thickness in 66.6% of cases, while atrophic lesions often showed decreased dermal thickness (78.5%), with significant differences (p<0.05). Dermal echogenicity decreased in 40.7% of active lesions but remained largely unchanged in atrophic lesions (82.1%) (p<0.05). Subcutaneous tissue thickness significantly decreased in atrophic lesions (78.5%) and increased in 59.2% of active lesions, with a significant difference (p = 0.002). Subcutaneous tissue echogenicity increased in 44.4% of active lesions and remained mostly unchanged in atrophic lesions (67.8%). Importantly, a considerable proportion of lesions diagnosed as active through physical examination were actually inactive on HFUS evaluation (55.6%), while a significant portion of lesions categorized as atrophic on physical examination displayed areas of inactivity upon ultrasonographic assessment (35.7%). These findings highlight HFUS's potential as a valuable diagnostic tool and reveal discordances between clinical and HFUS staging.

Ultrasonography offers an objective LS lesion evaluation, especially in pediatrics.

Evidence of the association between obesity and renal cell carcinoma progression is contradictory. The effects of renal cell carcinoma on fat distribution are still unknown.

The goal of this study was to determine the ability of various forms of fat deposition to predict the Fuhrman nuclear grade of clear cell renal cell carcinoma [ccRCC].

This retrospective study included 320 patients with pathologically proven ccRCC [215 men and 105 women; 263 low-grade ccRCC and 57 high-grade ccRCC]. Based on computed tomography scans, adipose tissue in various body regions was classified into the perirenal fat area [PFA], visceral fat area [VFA], total fat area [TFA], subcutaneous fat area [SFA], and hepatic steatosis [HS]. Subsequently, the relative VFA [rVFA] was computed. Age, sex, body mass index, and maximal tumor diameter were also regarded as clinical factors. Univariate and multivariate logistic regression studies were conducted to evaluate whether there was an association between body fat composition and the Fuhrman classification and whether it was related to gender.

After correcting for age, males with low-grade ccRCC exhibited higher TFA [257.6 vs. 203.0, p = 0.002], VFA [151.6 vs.115.5, p = 0.007], SFA [106.0 vs. 87.5, p = 0.015], PFA [55.1 vs. 30.4, p < 0.001], and HS [18% vs. 0%, p = 0.031] than those with high-grade ccRCC. There was no significant difference among rVFA in males. In females, there was no significant difference in any of the parameters. VFA and PFA remained independent predictors for high-grade ccRCC in males in both the monovariate [VFA: odds ratio [OR] 0.992, 95% confidence interval [CI] 0.987–0.997, p = 0.004; PFA: OR 0.949, 95% CI 0.930–0.970, p < 0.001] and multivariate [VFA: OR 1.028, 95% CI 1.001–1.074, p < 0.001; PFA: OR 0.878, 95% CI 0.833–0.926, p < 0.001] models.

Gender-specific adipose tissue in different locations demonstrated varied values for predicting high-grade ccRCC and may be utilized as an independent predictor of high-grade ccRCC in male patients.

Angiomatoid fibrous histiocytoma (AFH) is a borderline tumor usually affecting the the children or young adults. ¹⁸F-Fluorodexoyglucose (FDG) positron emission tomography/computed tomography (PET/CT) investigations of pulmonary AFH are rare, and there are currently no reports of intense FDG uptake in AFH.

We report an AFH that occurred in the lung of a 57-year-old woman. She presented with paroxysmal cough and occasional bloodshot sputum. ¹⁸F-FDG PET/CT revealed a right parahilar nodule with intense FDG-avidity, middle lobe atelectasis, and several bilateral axillary lymph nodes with mild hypermetabolic activity. This patient underwent a right middle lobe lobectomy via video-assisted thoracoscopy. Histopathologically, the diagnosis was pulmonary AFH. She had an uneventful postoperative course, and the bilateral axillary lymph nodes regressed during postoperative follow-up.

The clinical presentation and image findings of patients with primary pulmonary AFH may be potential diagnosis pitfalls. The diagnosis of lymph nodes or distant metastases should be approached with caution. To avoid misdiagnosis, biopsy with histological examination and immunohistochemichal staining should be performed as early as possible.

Early disease detection is emphasized within ophthalmology now more than ever, and as a result, clinicians and innovators turn to deep learning to expedite accurate diagnosis and mitigate treatment delay. Efforts concentrate on the creation of deep learning systems that analyze clinical image data to detect disease-specific features with maximum sensitivity. Moreover, these systems hold promise of early accurate diagnosis and treatment of patients with common progressive diseases. DenseNet, ResNet, and VGG-16 are among a few of the deep learning Convolutional Neural Network (CNN) algorithms that have been introduced and are being investigated for potential application within ophthalmology.

In this study, the authors sought to create and evaluate a novel ensembled deep learning CNN model that analyzes a dataset of shuffled retinal color fundus images (RCFIs) from eyes with various ocular disease features (cataract, glaucoma, diabetic retinopathy). Our aim was to determine (1) the relative performance of our finalized model in classifying RCFIs according to disease and (2) the diagnostic potential of the finalized model to serve as a screening test for specific diseases (cataract, glaucoma, diabetic retinopathy) upon presentation of RCFIs with diverse disease manifestations.

We found adding convolutional layers to an existing VGG-16 model, which was named as a proposed model in this article that, resulted in significantly increased performance with 98% accuracy (p<0.05), including good diagnostic potential for binary disease detection in cataract, glaucoma, diabetic retinopathy.

The proposed model was found to be suitable and accurate for a decision support system in Ophthalmology Clinical Framework.

The growing rate of breast cancer necessitates immediate global attention. Mammography images are used to determine the stage of malignancy. Breast cancer stages must be identified in order to save a person's life.

This article's main goal is to identify different techniques to obtain the difference between two breast cancer mammography images taken of the same individual at different times. This is the first effort to identify breast cancer in mammography images using change detection techniques. The Mammogram Image Change Detection (ICD) technique is also a recent advancement to prevent breast cancer in the early stage and precancerous level in medical images.

The main purpose of this work is to observe the changes between breast cancer images in different screening periods using different techniques. Mammogram Breast Cancer Image Change Detection (MBCICD) methods usually start with a Difference Image (DI) and classify the pixels in the DI into changed and unaffected classes using unsupervised fuzzy c means (FCM) clustering methods based on texture features taken from the log and mean ratio difference pictures. Two operators, mean ratio and log ratio, were used to check the changes in the images. The Gabor wavelet is utilized as a feature extraction technique among several standards. Using the Gabor wavelet ratio operators is a useful method for altering the detection of breast cancer in mammography images. Currently, it is challenging to obtain real malignant images of the same person for testing or training. In this study, two images are utilized. To clearly see the changes, one is an image from the MIAS breast cancer mammography images dataset, and the other is a self-generated change image.

The research aims to examine the image results and other quantitative analysis results of proposed change detection methods on cancer images. The Mean Ratio Accuracy result is 0.9738, and the Log ratio PCC is 0.9737. The classification results are the Log Ratio + Gabor Filter + FCM is 0.9737, and Mean Ratio +Gabor Filter + FCM is 0.9719. The mean Ratio Accuracy result is 0.9738, Log ratio is 0.9737. Log Ratio + Gabor Filter + FCM is 0.9737, Mean Ratio +Gabor Filter + FCM is 0.9719. Comparing the PCC of proposed change detection methods with the FDA-RMG method on the same dataset, the accuracy is 0.9481 only.

The study concludes that variations in mammography breast cancer images could be successfully identified using the ratio operators with Gabor wavelet features.

To investigate the magnetic resonance imaging (MRI) radiomics models in evaluating the human epidermal growth factor receptor 2(HER2) expression in breast cancer.

The MRI data of 161 patients with invasive ductal carcinoma (non-special type) of breast cancer were retrospectively collected, and the MRI radiomics models were established based on the MRI imaging features of the fat suppression T2 weighted image (T2WI) sequence, dynamic contrast-enhanced (DCE)-T1WIsequence and joint sequences. The T-test and the least absolute shrinkage and selection operator (LASSO) algorithm were used for feature dimensionality reduction and screening, respectively, and the random forest (RF) algorithm was used to construct the classification model.

The model established by the LASSO-RF algorithm was used in the ROC curve analysis. In predicting the low expression state of HER2 in breast cancer, the radiomics models of the fat suppression T2WI sequence, DCE-T1WI sequence, and the combination of the two sequences showed better predictive efficiency. In the receiver operating characteristic (ROC) curve analysis for the verification set of low, negative, and positive HER2 expression, the area under the ROC curve (AUC) value was 0.81, 0.72, and 0.62 for the DCE-T1WI sequence model, 0.79, 0.65 and 0.77 for the T2WI sequence model, and 0.84, 0.73 and 0.66 for the joint sequence model, respectively. The joint sequence model had the highest AUC value.

The MRI radiomics models can be used to effectively predict the HER2 expression in breast cancer and provide a non-invasive and early assistant method for clinicians to formulate individualized and accurate treatment plans.

Dermatofibrosarcoma Protuberans (DFSP) is a rare soft tissue sarcoma, accounting for approximately 1% of all tumors; however, DFSP of the breast is extremely rare. Moreover, DFSP generally has a low malignant potential and is characterized by a high rate of local recurrence along with a small but definite risk of metastasis. The risk of metastasis is higher in fibrosarcomatous transformation in DFSP than in ordinary DFSP.

We have, herein, reported a case of a 61-year-old male patient with fibrosarcomatous transformation in DFSP. Preoperative Dynamic Contrast-enhanced Magnetic Resonance Imaging (DCE-MRI) of the breast revealed an oval-shaped mass with heterogeneous internal enhancement, a large vessel embedded within, and a washout curve pattern on kinetic curve analysis. The mass exhibited a hyperintense signal on Diffusion-weighted Imaging (DWI), with a low apparent diffusion coefficient value. Histologically, the bland spindle tumor cells were arranged in a storiform pattern. Areas with the highest histological grade demonstrated increased cellularity, cytological atypia, and mitotic activity. Immunohistochemically, Ki-67 and p53 were highly expressed.

Recognizing the risk and accurately diagnosing fibrosarcomatous transformation in male breast DFSP are critical for improving prognosis and establishing appropriate treatment and follow-up plans. This emphasizes the significance of combining immunohistopathological features with DCE-MRI and DWI to assist clinicians in the early and accurate diagnosis of sarcomas arising from male breast DFSP.

To develop and evaluate machine learning models using tumor and nodal radiomics features for predicting the response to neoadjuvant chemotherapy (NAC) and recurrence risk in locally advanced gastric cancer (LAGC).

Early and accurate response prediction is vital to stratify LAGC patients and select proper candidates for NAC.

A total of 218 patients with LAGC undergoing NAC followed by gastrectomy were enrolled in our study and were randomly divided into a training cohort (n = 153) and a validation cohort (n = 65).

We extracted 1316 radiomics features from the volume of interest of the primary lesion and maximal lymph node on venous phase CT images. We built 3 radiomics signatures for distinguishing good responders and poor responders based on tumor radiomics (TR), nodal radiomics (NR), and a combination of the two (TNR), respectively. A nomogram was then developed by integrating the radiomics signature and clinical factors. Kaplan-Meier survival curves were used to evaluate the prognostic value of the nomogram.

The TNR signature achieved improved predictive value, with AUCs of 0.755 and 0.744 in the training and validation cohorts. Our proposed nomogram model (TNRN) showed a good performance for GR prediction in the prediction efficacy, calibration ability, and clinical benefit, with AUCs of 0.779 and 0.732 in the training and validation cohorts, superior to the clinical model. Moreover, the TNRN could accurately classify the patients into high-risk and low-risk groups in both training and validation cohorts with regard to postoperative recurrence and metastasis.

The TNRN performed well in identifying good responders and provided valuable information for predicting progression-free survival time (PFS) in patients with LAGC who underwent NAC.

To explore the potential of diffusion kurtosis imaging (DKI) for assessing the degree of liver injury in a paracetamol-induced rat model and to simultaneously investigate the effect of intravenous gadoxetate on DKI parameters.

Paracetamol was used to induce hepatoxicity in 39 rats. The rats were pathologically classified into 3 groups: normal (n=11), mild necrosis (n=18), and moderate necrosis (n=10). DKI was performed before and, 15 min, 25 min, and 45 min after gadoxetate administration. Repeated-measures ANOVA with Tukey’s multiple comparison test was used to investigate the effect of gadoxetate on mean diffusivity (MD) and mean diffusion kurtosis (MK) and to assess the differences in MD and MK among the three groups. A receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic accuracy of the MD values when discriminating between the necrotic groups.

Gadoxetate had no significant effect on either the MD or the MK, and the effect size was small. The MD in the moderate necrosis group was significantly lower than that in the other two groups (F = 13.502, p <0.001; η² = 0.428 [95% CI: 0.082-0.637]), while the MK did not significantly differ among the three groups (F = 2.702, p = 0.081; η² = 0.131 [95% CI: 0.001-0.4003]). The AUCs of MD for discriminating the moderate necrosis or normal group from the other groups were 0.921 (95% CI: 0.832-1.000) and 0.831 (95% CI: 0.701-0.961), respectively.

It would be better to measure the MD and MK before gadoxetate injection. MD showed potential for assessing the degree of liver necrosis in a paracetamol-induced liver injury rat model.

This study aims to develop an ultrasomics model for predicting lymph node metastasis in patients with gastric cancer (GC).

This study enrolled GC patients who underwent preoperative ultrasound examination. Manual segmentation of the region of interest (ROI) was performed by an experienced radiologist to extract radiomics features using the Pyradiomics software. The Z-score algorithm was used for feature normalization, followed by the Wilcoxon test to identify the most informative features. Linear prediction models were constructed using the least absolute shrinkage and selection operator (LASSO). The performance of the ultrasomics model was evaluated using the area under curve (AUC), sensitivity, specificity, and the corresponding 95% confidence intervals (CIs).

A total of 464 GC patients (mean age: 60.4 years ±11.3 [SD]; 328 men [70.7%]) were analyzed, of whom 291 had lymph node metastasis. The patients were randomly assigned to either the training (n=324) or test (n=140) sets, using a 7:3 ratio. An ultrasomics model that consisted of 19 radiomics features was developed using Wilcoxon and LASSO algorithms in the training set. Our ultrasomics model showed moderate performance for lymph node metastasis prediction in both the training (AUC: 0.802, 95% CI: 0.752-0.851, P<0.001) and test sets (AUC: 0.802, 95% CI: 0.724-0.879, P<0.001). The calibration curve analysis indicated good agreement between the predicted probabilities of ultrasomics and actual lymph node metastasis status.

Our study highlights the potential of a machine learning-based ultrasomics model in predicting lymph node metastasis in GC patients, offering implications for personalized therapy approaches.

Idiopathic Sudden Sensorineural Hearing Loss (ISSNHL) is related to alterations in brain cortical and subcortical structures, and changes in brain functional activities involving multiple networks, which is often accompanied by tinnitus. There have been many in-depth research studies conducted concerning ISSNHL. Despite this, the neurophysiological mechanisms of ISSNHL with tinnitus are still under exploration.

The study aimed to investigate the neural mechanism in ISSNHL patients with tinnitus based on the alterations in intra- and inter-network Functional Connectivity (FC) of multiple networks.

Thirty ISSNHL subjects and 37 healthy subjects underwent resting-state functional Magnetic Resonance Imaging (rs-fMRI). Independent Component Analysis (ICA) was used to identify 8 Resting-state Networks (RSNs). Furthermore, the study used a two-sample t-test to calculate the intra-network FC differences, while calculating Functional Network Connectivity (FNC) to detect the inter-network FC differences.

By using the ICA approach, tinnitus patients with ISSNHL were found to have FC changes in the following RSNs: CN, VN, DMN, ECN, SMN, and AUN. In addition, the interconnections of VN-SMN, VN-ECN, and ECN-DAN were weakened.

The present study has demonstrated changes in FC within and between networks in ISSNHL with tinnitus, providing ideas for further study on the neuropathological mechanism of the disease.

In this study, a radiomics model was created based on High-Resolution Computed Tomography (HRCT) images to noninvasively predict whether the sub-centimeter pure Ground Glass Nodule (pGGN) is benign or malignant.

A total of 235 patients (251 sub-centimeter pGGNs) who underwent preoperative HRCT scans and had postoperative pathology results were retrospectively evaluated. The nodules were randomized in a 7:3 ratio to the training (n=175) and the validation cohort (n=76). The volume of interest was delineated in the thin-slice lung window, from which 1316 radiomics features were extracted. The Least Absolute Shrinkage and Selection Operator (LASSO) was used to select the radiomics features. Univariate and multivariable logistic regression were used to evaluate the independent risk variables. The performance was assessed by obtaining Receiver Operating Characteristic (ROC) curves for the clinical, radiomics, and combined models, and then the Decision Curve Analysis (DCA) assessed the clinical applicability of each model.

Sex, volume, shape, and intensity mean were chosen by univariate analysis to establish the clinical model. Two radiomics features were retained by LASSO regression to build the radiomics model. In the training cohort, the Area Under the Curve (AUC) of the radiomics (AUC=0.844) and combined model (AUC=0.871) was higher than the clinical model (AUC=0.773). In evaluating whether or not the sub-centimeter pGGN is benign, the DCA demonstrated that the radiomics and combined model had a greater overall net benefit than the clinical model.

The radiomics model may be useful in predicting the benign and malignant sub-centimeter pGGN before surgery.

This study aimed to evaluate the diagnostic value of X-Map reconstruction based on Dual-Energy Computed Tomography (DECT) in acute ischemic stroke (AIS).

Sixty-six cases of suspected AIS patients hospitalized from November, 2021 to April, 2022 were retrospectively selected. DECT, Computed Tomography Perfusion imaging (CTP), Computed Tomography Angiography (CTA), and MRI were all performed within 24 hours after symptom onset. As the gold standard for diagnosing AIS, a total of 53 patients were diagnosed with AIS based on the diffusion-weighted imaging positive results in MRI. The Chi-square test was used to evaluate the diagnostic efficacy of AIS among X-Map, CTP, and CTA.

In the 53 patients with confirmed ASI, a total of 72 lesions were detected, including in the frontal lobes (n=33), parietal lobes (n=7), temporal lobes (n=12), basal ganglia regions (n=12), thalamus (n=3), and pons (n=5). The case detection rate of X-Map for AIS was similar to that of CTP (p=0.151) but was significantly higher than that of CTA (p<0.001). In terms of diagnostic efficacy, among the total 66 patients enrolled, X-Map achieved a higher diagnostic sensitivity (85%) than CTP and CTA. However, CTP achieved the best diagnostic specificity (84.6%) and diagnostic accuracy (77.4%) among the diagnostic tools used.

X-Map provides a better or equal clinical value for the diagnosis of AIS as compared to CTA and CTP, respectively, highlighting its potential in clinical applications.

Ultrasound-guided microwave ablation (MWA) is recommended as a first-line treatment for early liver cancer due to its minimally invasive, efficient, and cost-effective nature. It utilizes microwave radiation to heat and destroy tumor cells as a local thermal therapy and offers the benefits of being minimally invasive, repeatable, and applicable to tumors of various sizes and locations. However, despite the efficacy of MWA, early recurrence after treatment remains a challenge, particularly when it occurs within a year and has a significant impact on the prognosis of the patient.

This study aimed to identify the risk factors for early recurrence after MWA in patients with hepatocellular carcinoma (HCC) and establish a predictive model.

A total of 119 patients with hepatocellular carcinoma (HCC) treated in the Department of Ultrasound at the First Affiliated Hospital of the Air Force Medical University from January, 2020 to April, 2022 were included in this study. Patients were categorized into the early recurrence group and the non-early recurrence group based on whether recurrence occurred within 1 year. We conducted univariate analysis on 29 variables. A predictive model was developed using multiple-factor logistic regression analysis, and a risk column graph was created.

A total of 28 patients were included in the early recurrence group, with an early recurrence rate of 23%. Tumor size ≥ 3cm, multiple tumors, AST > 35 U/L, low pathological differentiation, CD34 positive, Ki67 level, quantitative parameters mean transit time (mTT), and rise time (RT) were confirmed as risk factors affecting early recurrence after ablation (P < 0.05). Furthermore, the model constructed based on these 5 predictive factors, including tumor size, tumor number, pathological differentiation, CD34, and quantitative analysis parameter mTT, demonstrated good predictive ability, with an AUC of 0.93 in the training set and 0.86 in the validation set.

Our research indicates that the risk column graph can be utilized to predict the risk of early postoperative recurrence in patients after MWA. This contributes to guiding personalized clinical treatment decisions and provides important references for improving the prognosis of patients.

Post COVID-19, everyone needs to be aware of health. The condition of the human body is judged based on various health reports like X-ray, CT scan and MRI scan. Due to misplacement or loss of medical reports, there lies a high chance of improper diagnosis.

In order to avoid improper diagnosis, a novel data-hiding technique is proposed in this work. In the proposed method, the patient’s health records are hidden using polynomial theory in the patient photograph. This is used by doctors in telemedicine for better treatment at the right time. Image steganography is useful for hiding secret images and also for generating secret keys. This enables only the authorized people (patient and corresponding doctor) to access the reports using secret keys.

Four secret images (medical reports of the patient) are successfully embedded onto a single cover image (patient photo) with good quality. After embedding, the stego images look like cover images so that unauthorized persons will not be able to access the data, and hence, safe transmission is being carried out.

A patient's medical report plays an important role in proper medical treatment. Particularly in telemedicine, the safe transmission of patient reports without any loss or damage is necessary. The proposed method embeds reports of a patient in his/her photo and transmits them to the destination safely with a quality of 45.5 dB. This hiding method is helpful to avoid cyber crimes, illegal transactions, malpractices etc.

Accurately predicting the hepatocellular carcinoma (HCC) grade may facilitate the rational selection of treatment strategies. The diagnostic efficacy of the combination of Gadolinium ethoxybenzy diethylenetriamine pentaacetic (Gd-EOB-DTPA) enhancement T1 mapping and apparent diffusion coefficient (ADC) values in predicting HCC grade needs further validation.

This study aimed to assess the capacity of Gd-EOB-DTPA-enhanced T1 mapping and ADC values, both individually and in combination, to discriminate between different grades of HCC.

From July 2017 to February 2020, 96 patients (male, 83; mean age, 53.67 years; age range, 29-71 years) clinically diagnosed with HCC were included in the present study. All patients underwent Gd-EOB-DTPA-enhanced magnetic resonance imaging (MRI, including T1 mapping sequence) before surgery or biopsy. All the patients were categorized into 3 groups according to the pathological results (including 24 cases of well-differentiated HCCs, 59 cases of moderately differentiated HCCs, 13 cases of and poorly differentiated HCCs). The mean Gd-EOB-DTPA enhanced T1 values (∆T1=[(T1pre-T1post)/T1pre]×100%) and ADC values between different grading groups of HCC were calculated and compared. The area under the characteristics curve (AUC), the diagnostic threshold, sensitivity, and specificity of ΔT1 and ADC for differential diagnosis were analyzed.

Mean ∆T1 was 58% for well-differentiated HCCs, 50% for moderately-differentiated HCCs, and 43% for poorly-differentiated HCCs. ∆T1 showed statistical differences between the groups (P<0.001). The mean ADC values of the 3 groups were 1.11×10−3 mm2/s, 0.91×10−3 mm2/s, and 0.80×10−3mm2/s, respectively. ADC showed statistical differences between the groups (P<0.001). In discriminating well- differentiated group from the moderately differentiated group, the AUC of ∆T1 was 0.751 (95% CI: 0.642, 0.859), the AUC of ADC was 0.782 (95% CI: 0.671, 0.894), the AUC of combined model was 0.811 (95% CI: 0.709, 0.914). In discriminating the poorly differentiated group from the moderately differentiated group, the AUC of ∆T1 was 0.768 (95% CI: 0.634, 0.902), the AUC of ADC was 0.754 (95% CI: 0.603, 0.904), and the AUC of the combined model was 0.841 (95% CI: 0.729, 0.953).

Gd-EOB-DTPA enhanced T1 mapping, and ADC values have complementary effects on the sensitivity and specificity for identifying different HCC grades. A combined model of Gd-EOB-DTPA-enhanced MRI T1 mapping and ADC values could improve diagnostic performance for predicting HCC grades.

The aim of the study was to develop deep-learning neural networks to guide treatment decisions and for the accurate evaluation of tumor response to neoadjuvant chemoradiotherapy (nCRT) in rectal cancer using magnetic resonance (MR) images.

Fifty-nine tumors with stage 2 or 3 rectal cancer that received nCRT were retrospectively evaluated. Pathological tumor regression grading was carried out using the Dworak (Dw-TRG) guidelines and served as the ground truth for response predictions. Imaging-based tumor regression grading was performed according to the MERCURY group guidelines from pre-treatment and post-treatment para-axial T2-weighted MR images (MR-TRG). Tumor signal intensity signatures were extracted by segmenting the tumors volumetrically on the images. Normalized histograms of the signatures were used as input to a deep neural network (DNN) housing long short-term memory (LSTM) units. The output of the network was the tumor regression grading prediction, DNN-TRG.

In predicting complete or good response, DNN-TRG demonstrated modest agreement with Dw-TRG (Cohen’s kappa= 0.79) and achieved 84.6% sensitivity, 93.9% specificity, and 89.8% accuracy. MR-TRG revealed 46.2% sensitivity, 100% specificity, and 76.3% accuracy. In predicting a complete response, DNN-TRG showed slight agreement with Dw-TRG (Cohen’s kappa= 0.75) with 71.4% sensitivity, 97.8% specificity, and 91.5% accuracy. MR-TRG provided 42.9% sensitivity, 100% specificity, and 86.4% accuracy. DNN-TRG benefited from higher sensitivity but lower specificity, leading to higher accuracy than MR-TRG in predicting tumor response.

The use of deep LSTM neural networks is a promising approach for evaluating the tumor response to nCRT in rectal cancer.

This study aimed to investigate the pancreatic morphology and clinical characteristics to predict risk factors of type 2 diabetes mellitus (T2DM) based on magnetic resonance imaging.

A total of 89 patients (T2DM group) and 68 healthy controls (HC group) were included. The T2DM group was divided into a long-term T2DM group and a short-term T2DM group according to whether the illness duration was more than 5 years. The clinical characteristics were collected, including sex, age, fasting plasma glucose, glycosylated hemoglobin, and lipoproteins. The pancreatic morphological characteristics, including the diameters of the pancreatic head, neck, body, and tail, the angle of the pancreaticobiliary junction (APJ), and the types of pancreaticobiliary junction were measured. The risk prediction model was established by logistic regression analysis.

In the long-term T2DM group, the pancreatic diameters were smaller than the other two groups. In the short-term T2DM group, the diameters of the pancreatic tail and body were smaller than the HC group. The APJ, very low-density lipoprotein, and triglyceride levels in the two T2DM groups were greater than the HC group, and the APJ of the short-term T2DM group was smaller than the long-term T2DM group. Pancreatic diameters showed a negative correlation with illness duration. Logistic regression analysis revealed pancreatic body diameter was a protective factor, and APJ was a risk factor for T2DM. Prediction model accuracy was 90.20%.

The morphology of the pancreas is helpful to predict the risk of the onset of T2DM. The risk of onset of T2DM increases with smaller pancreatic body diameter and higher APJ.

Congenital ileal atresia is a rare neonatal disease, the most common type of intestinal malformation in newborns, and one of the most common causes of congenital intestinal obstruction. It can cause various digestive system symptoms, including abdominal distension, vomiting, abnormal bowel movements, etc. In severe cases, it can be life-threatening. A prenatal ultrasound examination can assist clinical diagnosis of congenital ileal atresia, and those with a clear prenatal diagnosis should undergo surgical treatment after birth.

We have, herein, reported two cases of congenital ileal atresia, both of which showed fetal intestinal dilation (>7mm) and excessive amniotic fluid on prenatal ultrasound. Both newborns underwent surgical treatment after delivery and were confirmed to have congenital ileal atresia during surgery. Due to the different prenatal ultrasound manifestations of the two patients, they were divided into two different subtypes based on intraoperative manifestations. We observed significant differences in the prognosis of the two patients after surgery.

Accurately locating and classifying ileal atresia using prenatal ultrasound is challenging; however, it plays an effective role in disease progression, gestational assessment, and prognosis. Accurately identifying intestinal diseases and/or the location of lesion sites through direct and indirect ultrasound findings in the fetal abdominal cavity is an important research direction for prenatal ultrasound.

To investigate the optimal B1,rms value of renal amide proton transfer-weighted (APTw) images and the reproducibility of this value, and to explore the utility of APT imaging of renal masses and kidney tissues.

APTw images with different B1,rms values were repeatedly recorded in 15 healthy volunteers to determine the optimal value. Two 4-point Likert scales (poor [1] to excellent [4]) were used to evaluate contour clarity and artifacts in masses and normal tissues. The APTw values of masses and normal tissues were then compared in evaluable images (contour clarity score > 1, artifacts score > 1). The APTw of malignant masses, normal tissues, and benign masses were calculated and compared with the Mann-Whitney U test.

The optimal scanning parameter of B1,rms was 2 μT, and the APTw images had good agreement in the volunteers. Our study of APTw imaging examined 70 renal masses (13 benign, 57 malignant) and 49 normal kidneys (including those from 15 healthy volunteers). The mean APTw value for renal malignant masses (2.28(1.55)) was different from that for benign masses (0.91(1.30)) (P<0.001), renal cortex (1.30 (1.25)) (P<0.001), renal medulla (1.64 (1.33)) (P<0.05), and renal pelvis (5.49 (2.65)) (P<0.001).

These preliminary data demonstrate that APTw imaging of the kidneys has potential use as an imaging biomarker for the differentiation of normal tissues, malignant masses, and benign masses.

Magnetic resonance enteroclysis (MRE) has been widely applied to diagnose Crohn’s disease (CD). Magnetic resonance (MR) at 3.0 T improves signal-to-noise ratio (SNR), shortens image acquisition time, and shows more advantages.

This study aimed to retrospectively analyze the diagnostic value of 3.0 T MR imaging for active CD.

48 CD patients hospitalized in our hospital from January 2021 to December 2022 were selected as the study subjects. These 48 CD patients underwent both double-balloon enteroscopy and 3.0 T MRE. All patients' arterial phase signal, venous phase signal, bowel wall, and bowel lumen of MRE were observed to identify whether they suffered from active CD. Based on the results of enteroscopy, the number of true positives, true negatives, false negatives, and false positives diagnosed by MRE were screened; next, the diagnostic accuracy, sensitivity, and specificity of MRE in assessing active CD were calculated.

Of the 48 patients, 39 were diagnosed with small bowel CD by MRE, which was not significantly different from the results of enteroscopy (P>0.05). According to MRE diagnostic results, the arterial phase predominantly presented high signal intensity, and the venous phase mainly presented low signal intensity or isointensity. Small bowel CD lesions were primarily characterized by bowel wall thickening, rare pneumatosis enhancement of the bowel wall, bowel lumen pneumatosis or dilatation, and rare strictures. Besides, MRE presented an accuracy of 93.75%, sensitivity of 97.37%, and specificity of 80.00% in diagnosing CD.

3.0 T MR imaging has diagnostic value for active CD and shows certain clinical application value.

Hydatidosis, a distinctive parasitic ailment, exhibits a broad range of imaging characteristics influenced by the growth stage, resultant complications, and tissue involvement. Its occurrence throughout the human anatomy underscores its ubiquitous propensity. Despite its relatively infrequent manifestation as diffuse hydatosis, the disease assumes particular significance in rural regions. Given its detrimental complications and resemblance to other cystic conditions, vigilance towards the potential presence of this ailment becomes imperative.

In 2022, a 12-year-old female patient residing in a village sought medical assistance for left flank pain. During the evaluation, an incidental discovery of a pancreatic cyst through sonography prompted further investigation. Subsequent abdominopelvic computed tomography (CT) scans identified multiple lesions consistent with hydatid cysts in various anatomical locations, including the pancreas, right atrium, ventricle of the heart, pericardium, and lung. Confirmation of the hydatid cysts was obtained through pathology examination and consideration of the patient's medical history, which included a previously diagnosed brain hydatid cyst. Treatment with albendazole was initiated, and the patient underwent cardiac surgical intervention. Unfortunately, the condition of the patient deteriorated, leading to septic shock and subsequent mortality.

In areas with a high prevalence of hydatid cysts, the presence of diverse lesions on radiologic assessments, despite negative serologic tests, should raise suspicion for this condition. Furthermore, understanding the importance of timely detection and intervention is crucial, as it greatly impacts patient prognosis,. In the advanced stages of the disease, particularly when cardiac involvement occurs, surgical excision of the cysts remains the sole therapeutic approach, albeit accompanied by certain complications. Through the utilization of various imaging modalities and early recognition and treatment, the need for more complex interventions can be minimized.

The pathogenesis of breast cancer is characterized by dysregulated cell proliferation, leading to the formation of a neoplastic mass. Conventional methodologies for analyzing carcinomatous distal areas within whole-slide images (WSIs) tissue regions may lack comprehensive insights.

This study aims to introduce an innovative methodology based on convolutional neural networks (CNN), specifically employing a CNN Modified ResNet architecture for breast cancer detection. The research seeks to address the limitations of existing approaches and provide a robust solution for the comprehensive analysis of tissue regions.

The dataset utilized in this study comprises approximately 275,000 RGB image patches, each standardized at 50x50 pixels. The CNN Modified ResNet architecture is implemented, and a comparative evaluation against diverse architectures is conducted. Rigorous validation tests employing established performance metrics are carried out to assess the proposed methodology.

The proposed architecture achieves a notable 89% accuracy in breast cancer detection, surpassing alternative methods by 2%. The results signify the efficacy and superiority of the CNN Modified ResNet model in analyzing carcinomatous distal areas within WSIs tissue regions.

In conclusion, this study demonstrates the potential of the CNN Modified ResNet architecture as an effective tool for breast cancer detection. The enhanced accuracy and comprehensive analysis capabilities make it a promising approach for advancing the understanding of neoplastic masses in WSIs tissue regions. Further research and validation could solidify its role in clinical applications and diagnostic procedures.

This case report describes a case of Müllerian duct cyst that occurred in a male retroperitoneum. The cyst lesion is rare and complicated with diaphragmatic hernia. Müllerian duct-derived cyst is a rare developmental disorder that is more common in male pelvic tissues and rare in the retroperitoneum. We investigated the important role of computerized tomography (CT) and magnetic resonance imaging (MRI) in preoperative diagnosis and disease prediction of this condition.

A 25-year-old male was found to have an abnormal occupying lesion in the left diaphragm in imaging examinations, usually healthy with no obvious clinical symptoms. X-ray examination showed a circular, high-density shadow near the left diaphragm. CT scan showed a soft tissue density shadow resembling a tumor in the left adrenal area, irregularly protruding into the chest cavity, with uneven density. MRI examination showed an irregular elongated T1 and T2 signal shadow in the left adrenal area. T2 fat suppression showed high signal intensity with unrestricted diffusion. Robotic-assisted laparoscopic surgery showed left retroperitoneal tumor resection. The patient recovered well postoperatively and had no recurrence after discharge follow-up.

The preclinical symptoms of retroperitoneal Müllerian cysts complicated by diaphragmatic hernia in young men are difficult to distinguish, and it is difficult to diagnose other similar cysts with imaging. The method of combined CT and MRI diagnosis guides the endoscopic robot-assisted minimally invasive surgery for excision of cysts to achieve accurate diagnosis and treatment of such diseases.

Although the amygdala has structural and functional abnormalities in Chronic Migraine (CM), less is known about the altered perfusion of the amygdala in CM.

The current study aimed to assess amygdala perfusion in CM using a contrast agent-free and quantitative approach.

15 Normal Controls (NC) and 13 patients with CM during the migraine interval were assessed for brain structure and subjected to 3D Pseudo-Continuous Arterial Spin Labeling (3D-PCASL) MR imaging. The Cerebral Blood Flow (CBF) value of the amygdala was automatically extracted based on the individual amygdala mask for all participants. The independent sample t-test, Receiver Operating Characteristic (ROC) curve, and correlation analysis were used to evaluate the perfusion changes in CM.

Bilateral amygdala cerebral perfusion was lower in CM (left amygdala, 42.21±4.49 ml/100mg/min; right amygdala, 42.38±4.41 ml/100mg/min) than in NC (left amygdala, 48.31±6.92 ml/100mg/min; right amygdala, 47.88±6.53 ml/100mg/min) (left, p = 0.01; right, p = 0.02). There was no significant correlation between the perfusion of bilateral amygdalas and the clinical variables. Also, there was no significant difference in the volume of bilateral amygdalas between the two groups. The Area Under the Curve (AUC) of the CBF values of the left and right amygdala was 0.78 (95%CI: 0.58-0.91) and 0.75 (95%CI: 0.55-0.89), respectively. The cut-off value was 44.24 ml/100mg/min (left amygdala, with sensitivity 76.90% and specificity 78.70%) and 46.75 ml/100mg/min (right amygdala, with sensitivity 92.3% and specificity 58.80%), respectively.

CM presented bilateral hypoperfusion in the amygdala, offering potential diagnostic value in distinguishing CM from NC. The 3D-PCASL could be regarded as a simple and efficient neuroimaging tool to assess the perfusion status in CM patients.

Positron Emission Tomography (PET) scan stands as a valuable diagnostic tool in nuclear medicine, enabling the observation of metabolic and physiological changes at a molecular level. However, PET scans have a number of drawbacks, such as poor spatial resolution, noisy images, scattered radiation, artifacts, and radiation exposure. These challenges demonstrate the need for optimization in image processing techniques.

Our objective is to identify the evolving trends and impacts of publication in this field, as well as the most productive and influential countries, institutions, authors, themes, and articles.

A bibliometric study was conducted using a comprehensive query string such as “positron emission tomography” AND “image processing” AND optimization to retrieve 1,783 publications from 1981 to 2022 found in the Scopus database related to this field of study.

The findings revealed that the most influential country, institution, and authors are from the USA, and the most prevalent theme is TOF PET image reconstruction.

The increasing trend in publication in the field of optimization of image processing in PET scans would address the challenges in PET scan by reducing radiation exposure, faster scanning speed, as well as enhancing lesion identification.

This study aimed to compare the parametric value of T2 with and without fat suppression (FS) on T2 mapping for the evaluation of extraocular muscles (EOMs) in mild thyroid-associated ophthalmopathy (TAO).

We prospectively recruited 44 consecutive patients with mild TAO seen between May 2020 and October 2022 and 26 healthy controls with no history of eye- or thyroid-related or other autoimmune diseases. Patients with mild TAO were subdivided into active and inactive groups based on their clinical activity scores. The T2 of each EOM was measured over a large and small area of interest on T2-mapping images with and without FS. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic efficacy of T2 for detecting TAO activity.

The T2 was significantly and heterogeneously higher in the active group than in the inactive and control groups (P < 0.05). FS-T2-mapping images had better signal display within and at the edges of the EOMs than those without FS. It was possible to observe high-signal aggregation visible in the periphery of some EOMs, and the central part showed relatively low signals. The maximum T2 measured in small or large areas with and without FS had good diagnostic efficacy for TAO activity, with that of no-FS being better (the area under the ROC curve of the maximum T2 measured in a small area and a large area without FS was 1.0 and 1.0 and P values of < 0.001 and < 0.001, respectively).

Maximal T2 with or without FS can facilitate the early clinical detection of mild TAO activity. The maximum T2 in a small area can facilitate active staging of patients with mild TAO.