Current Medical Imaging - Volume 21, Issue 1, 2025

Leptomeningeal metastasis (LM) is a severe complication of solid malignancies, including lung adenocarcinoma, characterized by poor prognosis and diagnostic challenges. This study assesses whether curvilinear peri-brainstem hyperintense signals on MRI are a characteristic feature of LM in lung adenocarcinoma patients.

This retrospective study analyzed data from multiple centers, encompassing lung adenocarcinoma patients with peri-brainstem curvilinear hyperintense signals on MRI between January 2016 and March 2022. Clinical and radiological data were reviewed, and diagnostic and survival outcomes were analyzed.

Eleven patients (45-76 years; 6 males and 5 females) were included. The mean interval from lung adenocarcinoma diagnosis to detection of brainstem signal was 1.59 years. Four patients had elevated carcinoembryonic antigen levels, and 6 showed systemic metastases. MRI revealed curvilinear hyperintense signals along the pons, medulla, and cerebral peduncles on T2-Weighted Imaging (T2WI), Fluid-Attenuated Inversion Recovery (FLAIR), and Diffusion-Weighted Imaging (DWI). Mild linear enhancement was observed in 4 of 6 patients undergoing contrast-enhanced MRI, and tumor cells were detected in 4 of 6 Cerebrospinal Fluid (CSF) samples. The mean survival time in 7 patients with follow-up data was 3.42 months. Two patients exhibited peri-brainstem calcifications on CT 4–6 months after MRI and died shortly after.

These findings suggest that peri-brainstem curvilinear hyperintense signals and mild linear enhancement may serve as radiological markers of LM in lung adenocarcinoma. This pattern may reflect tumor infiltration or secondary changes in the leptomeninges.

Peri-brainstem curvilinear hyperintense signals and mild linear enhancement on T2WI, FLAIR, and DWI may be characteristic of LM in lung adenocarcinoma. These findings suggest further evaluation with contrast-enhanced MRI or CSF analysis for confirmation.

This study aims to improve the accuracy of distinguishing Tuberculous Spondylitis (TBS) from Brucella Spondylitis (BS) by developing radiomics models using Deep Learning and CT images enhanced with Super-Resolution (SR).

A total of 94 patients diagnosed with BS or TBS were randomly divided into training (n=65) and validation (n=29) groups in a 7:3 ratio. In the training set, there were 40 BS and 25 TBS patients, with a mean age of 58.34 ± 12.53 years. In the validation set, there were 17 BS and 12 TBS patients, with a mean age of 58.48 ± 12.29 years. Standard CT images were enhanced using SR, improving spatial resolution and image quality. The lesion regions (ROIs) were manually segmented, and radiomics features were extracted. ResNet18 and ResNet34 were used for deep learning feature extraction and model training. Four multi-layer perceptron (MLP) models were developed: clinical, radiomics (Rad), deep learning (DL), and a combined model. Model performance was assessed using five-fold cross-validation, ROC, and decision curve analysis (DCA).

Statistical significance was assessed, with key clinical and imaging features showing significant differences between TBS and BS (e.g., gender, p=0.0038; parrot beak appearance, p<0.001; dead bone, p<0.001; deformities of the spinal posterior process, p=0.0044; psoas abscess, p<0.001). The combined model outperformed others, achieving the highest AUC (0.952), with ResNet34 and SR-enhanced images further boosting performance. Sensitivity reached 0.909, and Specificity was 0.941. DCA confirmed clinical applicability.

The integration of SR-enhanced CT imaging and deep learning radiomics appears to improve diagnostic differentiation between BS and TBS. The combined model, especially when using ResNet34 and GAN-based super-resolution, demonstrated better predictive performance. High-resolution imaging may facilitate better lesion delineation and more robust feature extraction. Nevertheless, further validation with larger, multicenter cohorts is needed to confirm generalizability and reduce potential bias from retrospective design and imaging heterogeneity.

This study suggests that integrating Deep Learning Radiomics with Super-Resolution may improve the differentiation between TBS and BS compared to standard CT imaging. However, prospective multi-center studies are necessary to validate its clinical applicability.

This study aimed to determine the depth of the SF, bone thicknesses in the buccal and lingual areas of the mandibular canal (MC), vertical positions of the SF and MC relative to each other, and the tooth level at which the deepest point of the SF was observed in the cross-sectional section.

440 cone beam computed tomography (CBCT) images were retrospectively evaluated. The depth of the SF was determined. The buccal bone thickness (BBT) and lingual bone thickness (LBT) of the MC were measured, and the tooth alignment of the deepest point of the SF and the vertical position of the SF and MC relative to each other were determined.

In both jaws, SF depth Type I ratios were lower in males than in females, and SF depth Type III ratios were higher than in females. When the relationship between the vertical position of the MC and the region where the SF was deepest was examined, it was observed that the MC was in an inferior position in most patients.

In order to reduce the complication rate in the SF region, the relevant region should be analyzed in detail with CBCT before surgical procedures. The main limitation of our study is that the number of men and women was not equal.

SF depth and BBT values in the right and left jaws were higher in males than in females. LBT was higher in females in the right jaw. As the depth of the SF increased, BBT and LBT values decreased.

The calcaneal apophysis develops through a complex ossification process during childhood growth, with multiple secondary ossification centers emerging in distinct temporal and spatial patterns. Its ossification patterns, fusion process, and associated pediatric injuries and osteochondral conditions have been well documented in the literature. This report presents a previously unreported case of bilateral unfused medial process of calcaneal apophysis incidentally discovered in an adolescent patient during evaluation for genu valgum. We aim to describe this unique presentation and discuss potential pathogenic mechanisms underlying this distinctive anatomical variation.

A 12-year-old female patient was referred for idiopathic bilateral genu valgum and ankle valgus deformity management, with no prior treatment history or symptoms. Initial radiographs showed bilateral symmetric deformities, while CT revealed bilateral separated apophyses (Lt.; 8.8 mm, Rt.; 9.4 mm) at the medial process of the calcaneus with sclerotic margins. No underlying bone pathology or structural abnormalities were identified.

The bilateral unfused medial processes of the calcaneal apophysis in this patient represent a novel anatomical variation occurring alongside coxa valga and genu valgum. Biomechanical research indicates that hindfoot eversion increases medial heel pressure by 15%, with valgus alignment generating 11-12% higher medial heel pressure compared to lateral regions. These altered pressure patterns may influence apophyseal development. Normally, the medial process develops around age 9-10 and fuses 12-24 months later, with complete fusion by ages 14-16 in females. Our patient's bilateral persistence of unfused apophysis deviated significantly from this timeline. This selective non-fusion pattern differed from known pathological conditions, thus warranting further investigation through systematic studies.

This case highlights a rare anatomical variant of bilateral unfused medial calcaneal apophyses discovered incidentally in an adolescent. While the clinical significance remains uncertain, the bilateral and symmetric nature of these findings suggests a developmental variant rather than a pathological condition. This observation contributes to our understanding of variations in calcaneal apophyseal development.

To evaluate the effectiveness of spectral-CT in assessing the severity of liver diseases in patients with chronic liver disease (CLD).

A total of 148 CLD patients who underwent spectral-CT were retrospectively recruited, including 40 non-advanced CLD (non-ACLD), 74 compensated ACLD (cACLD), and 34 decompensated ACLD (dACLD). Iodine concentrations in the liver and spleen were assessed on iodine (water) images during the equilibrium phase, which allowed for the calculation of liver and splenic extracellular volume fractions (ECV). We determined the total liver volume, liver segmental volume ratio, and splenic volume from portal phase images. Moreover, established non-invasive tests were also collected. Areas under receiver operating characteristic curve (AUCs) were employed to evaluate the diagnostic performance of CT parameters and non-invasive tests in predicting CLD severity. Additionally, we analyzed the correlations between CT parameters and non-invasive tests.

The spleen volume demonstrated the highest AUC (0.815, P<0.001) for distinguishing between non-ACLD and cALCD. Child-Pugh score exhibited the highest AUC (0.948, P<0.001) for distinguishing cALCD and dACLD. Splenic ECV exhibited the highest AUC (0.853, P<0.001) for distinguishing non-ALCD and ACLD. In contrast, the liver ECV showed strong correlations with the Fibrosis-4 Index (r=0.653, p<0.001) and the Aminotransferase-to-Platelet Ratio Index (r=0.607, p<0.001), while spleen ECV correlated more strongly with the Child-Pugh score (r=0.719, p<0.001) and the Albumin-Bilirubin Index (r=0.742, p<0.001).

Liver and splenic ECV can effectively reflect the dynamic progression of CLD and correlate well with non-invasive tests in these patients.

Spectral-CT liver and splenic ECV could serve as non-invasive imaging biomarkers for severity stratification.

Breast cancer is a major cause of mortality among women globally. While mammography remains the gold standard for detection, its interpretation is often limited by radiologist variability and the challenge of differentiating benign and malignant lesions. The study explores the use of Auto-Sklearn, an automated machine learning (AutoML) framework, for breast tumor classification based on mammographic radiomic features.

244 mammographic images were enhanced using Contrast Limited Adaptive Histogram Equalization (CLAHE) and segmented with Active Contour Method (ACM). Thirty-seven radiomic features, including first-order statistics, Gray-Level Co-occurance Matrix (GLCM) texture and shape features were extracted and standardized. Auto-Sklearn was employed to automate model selection, hyperparameter tuning and ensemble construction. The dataset was divided into 80% training and 20% testing set.

The initial Auto-Sklearn model achieved an 88.71% accuracy on the training set and 55.10% on the testing sets. After the resampling strategy was applied, the accuracy for the training set and testing set increased to 95.26% and 76.16%, respectively. The Receiver Operating Curve and Area Under Curve (ROC-AUC) for the standard and resampling strategy of Auto-Sklearn were 0.660 and 0.840, outperforming conventional models, demonstrating its efficiency in automating radiomic classification tasks.

The findings underscore Auto-Sklearn’s ability to automate and enhance tumor classification performance using handcrafted radiomic features. Limitations include dataset size and absence of clinical metadata.

This study highlights the application of Auto-Sklearn as a scalable, automated and clinically relevant tool for breast cancer classification using mammographic radiomics.

Fundus fluorescein angiography captures detailed images of fundus vasculature, enabling precise disease assessment. Translating fundus images to fundus fluorescein angiography images can assist patients unable to use contrast agents due to physical constraints, facilitating disease analysis. Previous studies on this translation task were limited by the use of only 17 image pairs for training, potentially restricting model performance.

Image pairs were collected from patients through a collaborating hospital to create a larger dataset. A fundus image to fundus fluorescein angiography translation model was developed using structure self-supervised representation cycle learning. This model focuses on vascular structures for self-supervised learning, incorporates an auxiliary branch, and utilizes cycle learning to enhance the main training pipeline.

Comparative evaluations on the test set demonstrate superior performance of the proposed model, with significantly improved Fréchet inception distance and kernel inception distance scores. Additionally, generalization experiments conducted on public datasets further confirm the model's advantages in various evaluation metrics.

The enhanced performance of the proposed model can be attributed to the larger dataset and the novel structure self-supervised cycle learning approach, which effectively captures vascular details critical for accurate translation. The model's robust generalization across public datasets suggests its potential applicability in diverse clinical settings. However, challenges such as computational complexity and the need for further validation in real-world scenarios warrant additional investigation to ensure scalability and clinical reliability.

The proposed model effectively translates fundus images to fundus fluorescein angiography images, overcoming limitations of small datasets in previous studies. This approach demonstrates strong generalization capabilities, highlighting its potential to aid in large-scale disease analysis and patient care.

Conventional skin tumor examination shows inherent limitations in accurately assessing tumor depth. HR-MRI offers superior soft tissue resolution and a comprehensive evaluation of skin cancer.

Patients confirmed by pathological diagnosis as non-melanoma skin cancer from January 2021 to December 2023 were enrolled. Patients in Group 1 received both HR-MRI and tumorectomy, while those in Group 2 received tumorectomy only. The exclusion criteria include patients with contraindications to magnetic resonance examination. MRI sequences included T1WI, T2WI, and T2WI fat suppression, and a dynamic contrast-enhanced(DCE) scan. The advantages of different sequences in evaluating the level of invasion were independently assessed by two radiologists. The advantages of different sequences in evaluating the level of invasion were independently assessed by two radiologists. Tumor size, shape, invasion, and dynamic curves were measured in a corresponding sequence. And tumor signal intensity was recorded in different sequences. For each group, the number of postoperative tissue sections, sections with positive margins, and cases of secondary surgery were recorded. For Group 1, pathological invasion levels were also recorded.

89 cases of non-melanoma skin cancer were collected, including 69 basal cell carcinoma (BBC) and 20 squamous cell carcinoma (SCC). There were 25 patients in group 1 and 59 patients in group 2. T1WI showed mainly isointensity or hypointensity for BCC and SCC. T2WI showed predominantly hyperintense, and T2WI with fat suppression all showed hyperintense. T2WI effectively showed the relationship between tumors and nearby structures, while fat-suppressed T2WI highlighted tumor contours. The positive rate of pathological sections and the rate of secondary resection in group 1 and group 2 were 9.7% and 20%, 23.1% and 44.1%. There was a higher consistency between tumor invasion levels observed by MRI and pathological results in the first group (p>0.75)

Advancements in skin tumor diagnosis and treatment reveal that some tumors penetrate deeper than traditional methods can detect, prompting interest in MRI research. HR-MRI, known for its excellent soft tissue resolution, proves useful in outlining tumors and determining their location, particularly with the T2 fat-suppressed sequence. The T2WI sequence effectively assesses skin invasion, aligning well with pathological findings, and this significantly reduces the need for subsequent surgical interventions.. This underscores HR-MRI's value as a preoperative tool. However, the study's small sample size is a limitation, and future research will include more cases for deeper insights.

Skin HR-MRI is valuable for non-melanoma skin cancer, providing accurate preoperative tumor scope assessment, and reducing the rate of secondary surgeries.

Magnetic Resonance Imaging (MRI) is a crucial method for clinical diagnosis. Different abdominal MRI sequences provide tissue and structural information from various perspectives, offering reliable evidence for doctors to make accurate diagnoses. In recent years, with the rapid development of intelligent medical imaging, some studies have begun exploring deep learning methods for MRI sequence recognition. However, due to the significant intra-class variations and subtle inter-class differences in MRI sequences, traditional deep learning algorithms still struggle to effectively handle such types of complex distributed data. In addition, the key features for identifying MRI sequence categories often exist in subtle details, while significant discrepancies can be observed among sequences from individual samples. In contrast, current deep learning based MRI sequence classification methods tend to overlook these fine-grained differences across diverse samples.

To overcome the above challenges, this paper proposes a fine-grained prototype network, SequencesNet, for MRI sequence classification. A network combining convolutional neural networks (CNNs) with improved vision transformers is constructed for feature extraction, considering both local and global information. Specifically, a Feature Selection Module (FSM) is added to the visual transformer, and fine-grained features for sequence discrimination are selected based on fused attention weights from multiple layers. Then, a Prototype Classification Module (PCM) is proposed to classify MRI sequences based on fine-grained MRI representations.

Comprehensive experiments are conducted on a public abdominal MRI sequence classification dataset and a private dataset. Our proposed SequencesNet achieved the highest accuracy with 96.73% and 95.98% in two sequence classification datasets, respectively, and outperform the comparative prototypes and fine-grained models. The visualization results exhibit that our proposed sequencesNet can better capture fine-grained information.

The proposed SequencesNet shows promising performance in MRI sequence classification, excelling in distinguishing subtle inter-class differences and handling large intra-class variability. Specifically, FSM enhances clinical interpretability by focusing on fine-grained features, and PCM improves clustering by optimizing prototype-sample distances. Compared to baselines like 3DResNet18 and TransFG, SequencesNet achieves higher recall and precision, particularly for similar sequences like DCE-LAP and DCE-PVP.

The proposed new MRI sequence classification model, SequencesNet, addresses the problem of subtle inter-class differences and significant intra-class variations existing in medical images. The modular design of SequencesNet can be extended to other medical imaging tasks, including but not limited to multimodal image fusion, lesion detection, and disease staging. Future work can be done to decrease the computational complexity and increase the generalization of the model.

VETC-positive has emerged as a novel predictor of HCC for poor prognosis. Enhanced CT is one of the most common diagnostic methods, which can indicate VETC positivity, providing important evidence for the diagnosis and treatment of VETC-positive HCC.

The objective of this study is to investigate the clinical and preoperative enhanced CT imaging characteristics and diagnostic value of VETC-positive hepatocellular carcinoma (HCC) patients.

A retrospective analysis was conducted on the clinical, pathological, and imaging data of 53 HCC patients from the First Affiliated Hospital of Ningbo University between June 2019 and September 2022. According to pathological results, patients were categorized into 11 VETC-positive and 42 VETC-negative cases. Observational parameters included: (1) Clinical indicators: gender, age, history of hepatitis B virus infection, preoperative AFP, TNM staging, and preoperative biochemical and coagulation laboratory tests, including Alb, AST, ALT, TBil, DB, PT, TT, and INR. Additionally, pathological results such as histological grading, microvascular invasion (MVI), satellite nodules, neural invasion, and postoperative recurrence were analyzed. (2) Preoperative enhanced CT observational indicators: maximum tumor diameter, intrahepatic growth, irregular tumor margins, peritumoral hepatic parenchymal enhancement, mosaic structure, non-ring-like arterial phase hyperenhancement, marked heterogeneous enhancement, non-peripheral washout, absence of enhancing capsule, enhancing/clear capsule, intratumoral arteries, intratumoral necrosis, along with measurement of unenhanced CT values and enhanced CT values at various phases, calculating enhancement ratios (enhancement ratio = enhanced CT value - unenhanced CT value / unenhanced CT value).

Quantitative data were expressed as mean ± standard deviation (x̅±s), with intergroup comparisons conducted using the t-test; categorical variables were compared using the χ² test or Fisher's exact test. Multivariate analysis employed stepwise regression for logistic regression, incorporating clinical and imaging characteristics into the logistic regression equation. Based on logistic regression results, receiver operating characteristic (ROC) curves were plotted, calculating the area under the curve (AUC), sensitivity, specificity, and their 95% confidence intervals (CI). Analysis on survival was performed using Kaplan-Meier methods and log-rank tests, aiming survival curves.

(1) Clinical characteristics of VETC-positive versus VETC-negative patients: Preoperative AFP levels showed statistical significance (P=0.037), while no significant differences were observed in gender, age, Alb, TB, DB, AST, ALT, PT, TT, and INR between VETC-positive and VETC-negative patients (P>0.05). (2) Enhanced CT imaging features of VETC-positive versus VETC-negative patients: Intratumoral necrosis showed statistical significance (P<0.05), with intratumoral arteries being 63.6% (7/11) in the positive group compared to 42.9% (18/42) in the negative group. No significant differences were found in maximum tumor diameter, irregular tumor margins, peritumoral hepatic parenchymal enhancement, mosaic structure, non-ring-like arterial phase hyperenhancement, marked heterogeneous enhancement, non-peripheral washout, absence of enhancing capsule, enhancing capsule, intratumoral arteries, as well as unenhanced CT values and enhanced CT values at various phases, arterial phase enhancement ratio, portal phase enhancement ratio, and delayed phase enhancement ratio (P>0.05). (3) Multivariate analysis influencing VETC positivity: Arterial phase CT values (HU) (OR=0.937, P=0.029), intratumoral arteries (OR=9.452, P=0.021), and intratumoral necrosis (OR=0.013, P=0.003) were identified as independent risk factors for VETC positivity (Odds Ratio=0.937, 9.452, 0.013, 95% CI=0.883-0.993, 1.4-63.823, 0.001-0.223, P<0.05). The AUC of VETC was 0.863 (95% CI: 0.728-0.997), with a sensitivity of 81.8% and specificity of 88.1%. (4) Postoperative early tumor recurrence in VETC-positive and VETC-negative patients: All 53 patients were followed up, with an average tumor recurrence time of 11 (4-20) months, showing significant differences (P<0.05).

As one of the routine and preferred methods for HCC examination, enhanced CT plays a pivotal role in diagnosis, staging, and post-treatment evaluation. Combining preoperative enhanced arterial phase CT values, intratumoral arteries, and intratumoral necrosis can highly indicate VETC positivity.

In nuclear medicine, Prostate-specific Membrane Antigen (PSMA) is a potential target for theranostics. Offering superior diagnostic accuracy to conventional imaging in prostate cancer (PCa), Gallium-68 labeled PSMA (⁶⁸Ga-PSMA) positron emission tomography/computed tomography (PET/CT) is considered the new standard of care in PCa management. Tumor cells identified as PSMA-avid on PET/CT imaging can be targeted and eliminated with PSMA-labeled Lutetium-177 (¹⁷⁷Lu-PSMA) therapy.

A sixty-eight years old patient who had metastatic castration-resistant PCa was reported in this study. Prior to receiving ¹⁷⁷Lu-PSMA therapy, the patient’s PSA level was 358 ng/ml, and experienced extensive bone discomfort. Following ten cycles of ¹⁷⁷Lu-PSMA therapy, exceptional results were observed.

¹⁷⁷Lu-PSMA therapy is likely to result in significantly better outcomes if first- or second-line treatments preserve the patient's bone marrow reserve or if the therapy is administered at earlier stages of the disease.

The Myocardial Salvage Index (MSI) is a valuable indicator in ST-segment Elevation Myocardial Infarction (STEMI) treated with Percutaneous Coronary Intervention (PCI), yet challenges exist in its acquisition. This study aims to calculate MSI using Coronary Angiography (CAG) and myocardial perfusion imaging, and further investigate its correlation with long-term cardiac function.

In 203 STEMI, the myocardium at risk was measured through CAG using the Bypass Angioplasty Revascularization Investigation Myocardial Jeopardy Index (BARI) score. The infarcted myocardium was measured by the Total Perfusion Deficit (TPD) obtained in Myocardial Perfusion Imaging (MPI) after PCI. MSI was computed as (BARI score–TPD)/BARI score. Long-term cardiac function was assessed via echocardiography.

The MSI is notably associated with the long-term cardiac function [EF: Beta = 16 (13, 20), P < 0.00; LVD: Beta = -7.3 (-9.3, -5.3), P < 0.001]. TIMI flow grades 2-3 demonstrate a superior MSI compared to grades 0-1 [0.78 (0.32) vs. 0.61 (0.38), P = 0.002]. TIMI flow grades have an impact on MSI [Beta = 0.08 (0.04, 0.13), P < 0.001]. Compared to patients with a Killip grade of < 2, those with a grade ≥ 2 exhibit a lower MSI [0.69 (0.35) vs. 0.48 (0.42), p = 0.005]. The Killip classification has an impact on MSI [Beta = -0.12(-0.19, -0.04), P = 0.003].

The study indicates the pivotal role of MSI in predicting long-term cardiac function in STEMI, compares the advantages and limitations of SPECT, CMR, and hybrid SPECT/CAG methods, analyzes the impact of residual blood flow and acute heart failure on MSI, and highlights current technological challenges and future research directions.

CAG combining MPI after PCI can be used to obtain MSI. MSI is linked to long-term cardiac function. The amount of antegrade flow before PCI and the initial cardiac function upon admission significantly influence MSI.

This study aimed to assess the effectiveness and precision of a deep learning-based model in forecasting the early response of HCC patients to TACE.

A comprehensive review of HCC-TACE data involving 111 patients with HCC was carried out, encompassing both pre-TACE MR images (captured before the first TACE) and post-TACE imaging (acquired between 30 and 60 days following TACE). Based on the mRECIST criteria, patients were divided into two cohorts: a training dataset (91 subjects, 645 images) and a test dataset (20 subjects, 155 images). A deep learning-based model utilizing LeNet architecture with an attention mechanism was developed, targeting the prediction of HCC patients' response to TACE. The robustness and accuracy of the model were examined via ROC curves and confusion matrices.

Post-TACE treatment, 56 patients (50.5%) manifested an objective response (CR+PR), whereas 55 patients (49.5%) exhibited no response (SD+PD). Concerning the model's predictive ability for TACE response, the AUC was found to be 0.760 in the training dataset and 0.729 in the test dataset. The model's prediction accuracy was further corroborated by the confusion matrix, revealing an average accuracy of 70.7% in the training dataset and 72.3% in the test dataset.

Implementing a deep learning-based model using MRI data is potent for forecasting HCC patients’ response to TACE treatment. The novel LeNet model with the attention mechanism conceived in this study contributes valuable insights that can guide the formulation of effective treatment strategies.

Unilateral upper ureteral obstruction is one of the most common causes of acute kidney function impairment. Grading perirenal changes secondary to acute unilateral upper urinary tract obstruction (AUUTO) with multidetector spiral computed tomography (MDCT) and exploring its association with kidney function are useful for diagnosing and assessing damage to the ipsilateral kidney. However, the correlation between renal function impairment and the severity of perinephric changes secondary to AUUTO has not been reported.

This study aimed to investigate the association of perirenal changes secondary to AUUTO with hydronephrosis and serum creatinine levels, as well as white blood cell counts.

This retrospective study included 376 patients with acute unilateral upper ureteral obstruction, all of whom were subjected to MDCT scans. They were classified into four grades (0-III) according to their perirenal changes on MDCT images. The severity of hydronephrosis was classified into four grades based on MDCT scans. The serum creatinine level and leukocyte counts were compared among the MDCT grade groups, and logistic regression analysis was conducted.

Among 376 patients, 77 (20.5%), 103 (27.4%), 140 (37.2%), and 56 (14.9%) cases were graded into MDCT 0, I, II, and III, respectively. The proportions of patients who had normal kidneys in MDCT 0, I, II, and III were 20 (26.0%), 10 (9.7%), 11(7.9%), and 3 (5.4%), respectively. The proportions of patients who had mild hydronephrosis in MDCT 0, I, II, and III were 55 (71.4%), 83 (80.6%), 118 (84.2%), and 46 (82.1%), respectively. The proportions of patients who had moderate and severe hydronephrosis in MDCT 0, I, II, and III were 2(2.6%), 10 (9.7%), 11 (7.9%), 7 (12.5%), respectively. Serum creatinine levels and white blood cell counts were significantly different among the MDCT grade groups (P < 0.001). Univariate and multivariate logistic regression analyses indicated that the serum creatinine level and white blood cell counts were positively associated with the MDCT grades (P < 0.001).

Perinephric changes secondary to AUUTO on MDCT images were associated with the degree of obstruction. The severity of perinephric changes can reflect the functional impairment in the ipsilateral kidney. The MDCT grades may aid clinicians in assessing renal function impairment early in patients with AUUTO, which may help patients receive early intervention and avoid the potential risk of infection and deterioration of renal function.

The incidence of Alzheimer’s disease is rising with the increasing elderly population worldwide. While no cure exists, early diagnosis can significantly slow disease progression. Computer-aided diagnostic systems are becoming critical tools for assisting in the early detection of Alzheimer’s disease. In this systematic review, we aim to evaluate recent advancements in computer-aided decision support systems for Alzheimer’s disease diagnosis, focusing on data modalities, machine learning methods, and performance metrics.

We conducted a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Studies published between 2021 and 2024 were retrieved from PubMed, IEEEXplore and Web of Science, using search terms related to Alzheimer’s disease classification, neuroimaging, machine learning, and diagnostic performance. A total of 39 studies met the inclusion criteria, focusing on the use of Magnetic Resonance Imaging, Positron Emission Tomography, and biomarkers for Alzheimer’s disease classification using machine learning models.

Multimodal approaches, combining Magnetic Resonance Imaging with Positron Emission Tomography and Cognitive assessments, outperformed single-modality studies in diagnostic accuracy reliability. Convolutional Neural Networks were the most commonly used machine learning models, followed by hybrid models and Random Forest. The highest accuracy reported for binary classification was 100%, while multi-class classification achieved up to 99.98%. Techniques like Synthetic Minority Over-sampling Technique and data augmentation were frequently employed to handle data imbalance, improving model generalizability.

Our review highlights the advantages of using multimodal data in computer-aided decision support systems for more accurate Alzheimer’s disease diagnosis. However, we also identified several limitations, including data imbalance, small sample sizes, and the lack of external validation in most studies. Future research should utilize larger, more diverse datasets, include longitudinal data, and validate models in real-world clinical trials. Additionally, explainability is needed in machine learning models to ensure they are interpretable and reliable in clinical settings.

While computer-aided decision support systems show significant promise in improving the early diagnosis of Alzheimer’s disease, further work is needed to enhance their robustness, generalizability, and clinical applicability. By addressing these challenges, computer-aided decision support systems could play a key role in the early detection of Alzheimer’s disease and potentially reduce health care costs.

Adrenal incidentalomas have increased over the past years. Although there are a lot of studies related to the frequency of adrenal masses and comorbidities, whether patients with functional or nonfunctional adrenal masses have higher risk is still a controversial issue.

A total of 464 patients (female/male: 309/155) with adrenal incidentalomas were evaluated and followed up for 8 years. The patients were divided into 5 subgroups, including Autonomous Cortisol Secretion (ACS), Cushing Syndrome (CS), Pheochromocytoma (Pheo), Non-functional Adrenal Incidentalomas (NFAI), and Primary Aldosteronism (PA).

While 336 (72.4%) of the patients had NFAI, the others suffered from ACS (10.8%), CS (4.3%), Pheo (4.1%), and PA (8.4%), respectively. When comparing biochemical and demographical data, BMI (p=0.77), Hba1c (p=0.495), FPG (p=0.28), LDL (p=0.66), and HDL (p=0.521) were similar among the patients with functional and nonfunctional adrenal masses. The most common comorbidities were hypertension (n=259, 55.8%), diabetes mellitus (n=158, 34.1%), and dyslipidemia (33.4%), respectively. While 84 (32.4%) patients with hypertension had functional adrenal masses, the others (n=175, 67.6%) had non-functional adrenal incidentalomas. In subgroup analyses, hypertension was more common in patients with PA (87.2% vs. 72%, p=0.001) and ACS. In multivariable regression analyses, hypertension (p<0.001), cortisol (p=0.003), and aldosterone (p=0.04) levels were significantly correlated with functionality.

Hypertension was the most common comorbidity in patients with adrenal adenomas, especially in functional adrenal adenomas related to serum cortisol and aldosterone levels.

This study aimed to demonstrate the relationship between hemodynamic changes in head blood flow and headache during the premenstrual period in patients experiencing premenstrual syndrome.

Thirty-two female patients experiencing premenstrual headaches were prospectively examined using carotid and vertebral artery duplex ultrasonography during headache episodes in the premenstrual periods and headache-free periods across two consecutive menstrual cycles. The diameters and areas of both the carotid and vertebral arteries, along with systolic and end-diastolic velocities, pulsatility and resistivity indices, and volumetric flow rates, were measured using grayscale imaging. Total head blood flow was determined as the sum of bilateral common carotid artery and vertebral artery flow volumes. Measurements were compared between participants’ premenstrual and menstrual periods.

A statistically significant difference in the diameter of the left external carotid artery was observed between periods with and without headache during the two consecutive menstrual cycles assessed (p = 0.030). Left external carotid artery (p = 0.019), total external carotid artery (p = 0.028), and total head blood volumes (p = 0.030) were significantly higher when headache was present during the premenstrual period than when headache was absent.

Towards the end of the luteal phase, the total head blood flow and external carotid artery flow were high due to a decrease in peripheral resistance caused by the decline in progesterone and hormonal fluctuations during this period.

Increased flow volume in the external carotid arteries and total head blood flow may be a contributing factor to premenstrual headaches.

Post-hepatic resection liver failure (PHLF) remains one of the most serious complications after hepatic resection, with an overall morbidity rate as high as 32% and an approximate 5% mortality. Previous studies demonstrate the potential of shear wave elastography (SWE) to predict PHLF. This meta-analysis aimed to evaluate the diagnostic accuracy of SWE in identifying liver failure after hepatectomy.

A comprehensive search was performed across PubMed/Medline, Embase, and Web of Science to identify studies assessing the diagnostic accuracy of SWE for predicting PHLF. The combined sensitivity, specificity, and the hierarchical summary receiver operating characteristic curve (HSROC) for SWE in detecting PHLF in liver resection patients. The Quality Assessment of Diagnostic Accuracy Studies tool was used to evaluate the quality of the studies included in the analysis. Heterogeneity was explored through sensitivity analysis, univariable meta-regression and subgroup analysis.

This meta-analysis included a total of 13 studies involving 2985 patients. For quantitative analysis. The combined sensitivities and specificities of SWE for detecting post-hepatectomy liver failure were 0.81 and 0.68, respectively. The HSROC value for SWE was 0.82. Significant heterogeneity (I² = 80.22) was observed in pooled specificity. Meta-regression and subgroup analyses suggest that differences in the proportion of patients with HCC and in the diagnostic criteria for PHLF may account for the observed heterogeneity. For the qualitative analysis, six predictive models based on SWE were included, and their AUCs were 0.80-0.915.

Both SWE alone and SWE-based prediction models appear to accurately detect PHLF and help to categorize patients into high- and low-risk groups. It may also assist surgeons in identifying the best candidates for liver resection and enhancing perioperative management.

An accurate and reliable prognostic model for Nasal Extranodal Natural Killer/T-cell Lymphoma (ENKTL) is critical for survival outcomes and personalized therapy. Currently, there is no Magnetic Resonance Imaging (MRI)- based radiomics analysis in the prognosis model for nasal ENKTL patients.

We aim to explore the value of MRI-based radiomics signature in the prognosis of patients with nasal ENKTL.

A total of 159 nasal ENKTL patients were enrolled and divided into a training cohort (n=81) and a validation cohort (n=78) randomly. Radiomics features from pretreatment MRI examination were extracted, respectively. Then two-sample t-test and Least Absolute Shrinkage and Selection Operator (LASSO) regression were used to select the radiomics signatures and establish the Rad-score. Univariate and multivariate Cox proportional hazards regression models were used to investigate the prognostic value of baseline clinical features and establish clinical models. A radiomics nomogram based on the Rad-score and clinical features was constructed to predict Overall Survival (OS). The predictive efficacy of the three models was evaluated in two cohorts.

A total of 1,345 features were extracted from T2-weighted (T2-w) and Contrast-enhanced T1-weighted (CET1-w) images, respectively, and 1,037 features with Intraclass Correlation Coefficient (ICC) >0.7 were selected. Ultimately, 20 features were chosen to construct the Rad-score, which showed a significant association with OS. The C-indexes of the Rad-score were 0.733 (95% confidence interval (CI): 0.645 to 0.816) and 0.824 (95% CI: 0.766-0.882), respectively, in training and validation cohorts. Through the univariate and multivariate analyses, three independent risk factors for OS were identified: Rad-score (HR: 10.962, 95% CI: 3.417-35.167, P <0.001), lactate dehydrogenase (LDH) level (HR: 3.009, 95% CI: 1.128-8.510, P = 0.028) and distant lymph-node involvement (HR: 2.966, 95% CI: 1.015-8.664, P = 0.047). Patients with distal lymph node involvement and LDH level before treatment were included in the clinical model, which achieved a C-index of 0.707 (95% CI: 0.600–0.814) in the training cohort and 0.635 (95% CI: 0.527–0.743) in the validation cohort.

We integrated the Rad-score and clinical variables to establish a radiomics nomogram, which exhibited a satisfactory prediction performance with the C-indexes of 0.849(95% CI: 0.781-0.917) and 0.931 (95% CI: 0.882-0.980) in two cohorts, respectively. The radiomics nomogram was more accurate in predicting OS in patients with nasal ENKTL than the other two models. Based on the radiomics nomogram, patients were categorized into low-risk and high-risk groups in two cohorts (P all < 0.05). The high-risk group defined by this nomogram exhibited a shorter OS.

The Rad-score was significantly correlated with OS for nasal ENKTL patients. Moreover, the MRI-based radiomics nomogram could be used for risk stratification and might guide individual treatment decisions.