Prostate cancer remains a leading cause of mortality, necessitating precise histopathological segmentation for accurate Gleason Grade assessment. However, existing deep learning-based segmentation models lack contextual awareness and explainability, leading to inconsistent performance across heterogeneous tissue structures. Conventional U-Net architectures and CNN-based approaches struggle with capturing long-range dependencies and fine-grained histopathological patterns, resulting in suboptimal boundary delineation and model generalizability. To address these limitations, we propose a transformer-attention hybrid U-Net (TAH U-Net), integrating hybrid CNN-transformer encoding, attention-guided skip connections, and a multi-stage guided loss mechanism for enhanced segmentation accuracy and model interpretability. The ResNet50-based convolutional layers efficiently capture local spatial features, while Vision Transformer (ViT) blocks model global contextual dependencies, improving segmentation consistency. Attention mechanisms are incorporated into skip connections and decoder pathways, refining feature propagation by suppressing irrelevant tissue noise while enhancing diagnostically significant regions. A novel hierarchical guided loss function optimizes segmentation masks at multiple decoder stages, improving boundary refinement and gradient stability. Additionally, Explainable AI (XAI) techniques such as LIME, Occlusion Sensitivity, and Partial Dependence Analysis (PDP), validate the model's decision-making transparency, ensuring clinical reliability. The experimental evaluation on the SICAPv2 dataset demonstrates state-of-the-art performance, surpassing traditional U-Net architectures with a 4.6% increase in Dice Score, 5.1% gain in IoU, along with notable improvements in Precision (+ 4.2%) and Recall (+ 3.8%). This research significantly advances AI-driven prostate cancer diagnostics by providing an interpretable and highly accurate segmentation framework, enhancing clinical trust in histopathology-based grading within medical imaging and computational pathology.

To evaluate the translational capabilities of foundation models, we develop a pathological concept learning approach focused on kidney cancer. By leveraging TNM staging guidelines and pathology reports, we build comprehensive pathological concepts for kidney cancer. Then, we extract deep features from whole slide images using foundation models, construct pathological graphs to capture spatial correlations, and trained graph neural networks to identify these concepts. Finally, we demonstrate the effectiveness of this approach in kidney cancer survival analysis, highlighting its explainability and fairness in identifying low- and high-risk patients. The source code has been released by https://github.com/shangqigao/RadioPath.

Kidney lesion subtype identification is essential for precise diagnosis and personalized treatment planning. However, achieving reliable classification remains challenging due to factors such as inter-patient anatomical variability, incomplete multi-phase CT acquisitions, and ill-defined or overlapping lesion boundaries. In addition, genetic and ethnic morphological variations introduce inconsistent imaging patterns, reducing the generalizability of conventional deep learning models. To address these challenges, we introduce a unified framework called Phase-aware Cross-Scale U-MAMba and Switch Atrous Bifovea EfficientNet B7 (PCU-SABENet), which integrates multi-phase reconstruction, fine-grained lesion segmentation, and robust subtype classification. The PhaseGAN-3D synthesizes missing CT phases using binary mask-guided inter-phase priors, enabling complete four-phase reconstruction even under partial acquisition conditions. The PCU segmentation module combines Contextual Attention Blocks, Cross-Scale Skip Connections, and uncertainty-aware pseudo-labeling to delineate lesion boundaries with high anatomical fidelity. These enhancements help mitigate low contrast and intra-class ambiguity. For classification, SABENet employs Switch Atrous Convolution for multi-scale receptive field adaptation, Hierarchical Tree Pooling for structure-aware abstraction, and Bi-Fovea Self-Attention to emphasize fine lesion cues and global morphology. This configuration is particularly effective in addressing morphological diversity across patient populations. Experimental results show that the proposed model achieves state-of-the-art performance, with 99.3% classification accuracy, 94.8% Dice similarity, 89.3% IoU, 98.8% precision, 99.2% recall, a phase-consistency score of 0.94, and a subtype confidence deviation of 0.08. Moreover, the model generalizes well on external datasets (TCIA) with 98.6% accuracy and maintains efficient computational performance, requiring only 0.138 GFLOPs and 8.2 ms inference time. These outcomes confirm the model's robustness in phase-incomplete settings and its adaptability to diverse patient cohorts. The PCU-SABENet framework sets a new standard in kidney lesion subtype analysis, combining segmentation precision with clinically actionable classification, thus offering a powerful tool for enhancing diagnostic accuracy and decision-making in real-world renal cancer management.

Liver fibrosis represents the accumulation of excessive extracellular matrix caused by sustained hepatic injury. It disrupts normal lobular architecture and function, increasing the chances of cirrhosis and liver failure. Precise staging of fibrosis for early diagnosis and intervention is often invasive, which carries risks and complications. To address this challenge, recent advances in artificial intelligence-based liver segmentation and fibrosis staging offer a non-invasive alternative. As a result, the CARE 2025 Challenge aimed for automated methods to quantify and analyse liver fibrosis in real-world scenarios, using multi-centre, multi-modal, and multi-phase MRI data. This challenge included tasks of precise liver segmentation (LiSeg) and fibrosis staging (LiFS). In this study, we developed an automated pipeline for both tasks across all the provided MRI modalities. This pipeline integrates pseudo-labelling based on multi-modal co-registration, liver segmentation using deep neural networks, and liver fibrosis staging based on shape, textural, appearance, and directional (STAD) features derived from segmentation masks and MRI images. By solely using the released data with limited annotations, our proposed pipeline demonstrated excellent generalisability for all MRI modalities, achieving top-tier performance across all competition subtasks. This approach provides a rapid and reproducible framework for quantitative MRI-based liver fibrosis assessment, supporting early diagnosis and clinical decision-making. Code is available at https://github.com/YangForever/care2025_liver_biodreamer.

Pancreatic cancer is one of the most aggressive and lethal malignancies of the digestive system and is characterized by an extremely low five-year survival rate. The perineural invasion (PNI) status in patients with pancreatic cancer is positively correlated with adverse prognoses, including overall survival and recurrence-free survival. Emerging radiomic methods can reveal subtle variations in tumor structure by analyzing preoperative contrast-enhanced computed tomography (CECT) imaging data. Therefore, we propose the development of a preoperative CECT-based radiomic model to predict the risk of PNI in patients with pancreatic cancer. This study enrolled patients with pancreatic malignancies who underwent radical resection. Computerized tools were employed to extract radiomic features from tumor regions of interest (ROIs). The optimal radiomic features associated with PNI were selected to construct a radiomic score (RadScore). The model's reliability was comprehensively evaluated by integrating clinical and follow-up information, with SHapley Additive exPlanations (SHAP)-based visualization to interpret the decision-making processes. A total of 167 patients with pancreatic malignancies were included. From the CECT images, 851 radiomic features were extracted, 22 of which were identified as most strongly correlated with PNI. These 22 features were evaluated using seven machine learning methods. We ultimately selected the Gaussian naive Bayes model, which demonstrated robust predictive performance in both the training and validation cohorts, and achieved area under the ROC curve (AUC) values of 0.899 and 0.813, respectively. Among the clinical features, maximum tumor diameter, CA-199 level, blood glucose concentration, and lymph node metastasis were found to be independent risk factors for PNI. The integrated model yielded AUCs of 0.945 (training cohort) and 0.881 (validation cohort). Decision curve analysis confirmed the clinical utility of the ensemble model to predict perineural invasion. The combined model integrating clinical and radiomic features exhibited excellent performance in predicting the probability of perineural invasion in patients with pancreatic cancer. This approach has significant potential to optimize therapeutic decision-making and prognostic evaluation in patients with PNI.

High-Intensity Focused Ultrasound (HIFU) ablation has emerged as a non-invasive treatment option for uterine fibroids that preserves fertility and offers faster recovery. Pre-intervention prediction of HIFU efficacy can augment clinical decision-making and patient management. This systematic review and meta-analysis aims to evaluate the performance of MRI-based radiomics machine learning (ML) models in predicting the efficacy of HIFU ablation in uterine fibroids. Studies were retrieved by conducting a thorough literature search across databases including PubMed, Scopus, Embase, and Web of Science, up to June 2025. The quality of the included studies was assessed using the QUADAS-2 and METRICS tools. A meta-analysis of the radiomics models was conducted to pool sensitivity, specificity, and AUC using a bivariate random-effects model. A total of 13 studies were incorporated in the systematic review and meta-analysis. Meta-analysis of 608 patients from 7 internal and 6 external validation cohorts showed pooled AUC, sensitivity, and specificity of 0.84, 77%, and 78%, respectively. QUADAS-2 was notable for significant methodological biases in the index test and flow and timing domains. Across all studies, the mean METRICS score was 76.93%-with a range of 54.9%-90.3%-denoting good overall quality and performance in most domains but with notable gaps in the open science domain. MRI-based radiomics models show promise in predicting the effectiveness of HIFU ablation for uterine fibroids. However, limitations such as limited geographic diversity, inconsistent reporting standards, and poor open science practices hinder broader application. Therefore, future research should focus on standardizing imaging protocols, using multi-center designs with external validation, and integrating diverse data sources.

To develop and validate an ultrasonography-based machine learning (ML) model for predicting malignant endometrial and cavitary lesions. This retrospective study was conducted on patients with pathologically confirmed results following transvaginal or transrectal ultrasound from 2021 to 2023. Endometrial ultrasound features were characterized using the International Endometrial Tumor Analysis (IETA) terminology. The dataset was ranomly divided (7:3) into training and validation sets. LASSO (least absolute shrinkage and selection operator) regression was applied for feature selection, and an extreme gradient boosting (XGBoost) model was developed. Performance was assessed via receiver operating characteristic (ROC) analysis, calibration, decision curve analysis, sensitivity, specificity, and accuracy. Among 1080 patients, 6 had a non-measurable endometrium. Of the remaining 1074 cases, 641 were premenopausal and 433 postmenopausal. Performance of the XGBoost model on the test set: The area under the curve (AUC) for the premenopausal group was 0.845 (0.781-0.909), with a relatively low sensitivity (0.588, 0.442-0.722) and a relatively high specificity (0.923, 0.863-0.959); the AUC for the postmenopausal group was 0.968 (0.944-0.992), with both sensitivity (0.895, 0.778-0.956) and specificity (0.931, 0.839-0.974) being relatively high. SHapley Additive exPlanations (SHAP) analysis identified key predictors: endometrial-myometrial junction, endometrial thickness, endometrial echogenicity, color Doppler flow score, and vascular pattern in premenopausal women; endometrial thickness, endometrial-myometrial junction, endometrial echogenicity, and color Doppler flow score in postmenopausal women. The XGBoost-based model exhibited excellent predictive performance, particularly in postmenopausal patients. SHAP analysis further enhances interpretability by identifying key ultrasonographic predictors of malignancy.

The purpose of this study was to profile elemental composition of calcium-based urinary stones using laser-induced breakdown spectroscopy (LIBS) and develop a machine learning model to distinguish recurrence-associated profiles by integrating elemental and clinical data. A total of 122 calcium-based stones (41 calcium oxalate, 11 calcium phosphate, 49 calcium oxalate/calcium phosphate, 8 calcium oxalate/uric acid, 13 calcium phosphate/struvite) were analyzed via LIBS. Elemental intensity ratios (H/Ca, P/Ca, Mg/Ca, Sr/Ca, Na/Ca, K/Ca) were calculated using Ca (396.847 nm) as reference. Clinical variables (demographics, laboratory and imaging results, recurrence status) were retrospectively collected. A back propagation neural network (BPNN) model was trained using four data strategies: clinical-only, spectral principal components (PCs), combined PCs plus clinical, and merged raw spectral plus clinical data. The performance of these four models was evaluated. Sixteen stone samples from other medical centers were used as external validation sets. Mg and Sr were detected in most of stones. Significant correlations existed among P, Mg, Sr, and K ratios. Recurrent patients showed elevated elemental ratios (p < 0.01), higher urine pH (p < 0.01), and lower stone CT density (p = 0.044). The BPNN model with merged spectral plus clinical data achieved optimal performance in classification (test set accuracy: 94.37%), significantly outperforming clinical-only models (test set accuracy: 73.37%). The results of external validation indicate that the model has good generalization ability. LIBS reveals ubiquitous Mg and Sr in calcium-based stones and elevated elemental ratios in recurrent cases. Integration of elemental profiles with clinical data enables high-accuracy classification of recurrence-associated profiles, providing insights for potential risk stratification in urolithiasis management.

Medical image segmentation is a critical task for the early detection and diagnosis of various conditions, such as skin cancer, polyps, thyroid nodules, and pancreatic tumors. Recently, deep learning architectures have achieved significant success in this field. However, they face a critical trade-off between local feature extraction and global context modeling. To address this limitation, we present DCM-Net, a dual-encoder architecture that integrates pretrained CNN layers with Visual State Space (VSS) blocks through a Cross-Branch Feature Fusion Module (CBFFM). A Decoder Feature Enhancement Module (DFEM) combines depth-wise separable convolutions with MLP-based semantic rectification to extract enhanced decoded features and improve the segmentation performance. Additionally, we present a new 2D pancreas and pancreatic tumor dataset (CCH-PCT-CT) collected from Chongqing University Cancer Hospital, comprising 3,547 annotated CT slices, which is used to validate the proposed model. The proposed DCM-Net architecture achieves competitive performance across all datasets investigated in this study. We develop a novel DCM-Net architecture that generates robust features for tumor and organ segmentation in medical images. DCM-Net significantly outperforms all baseline models in segmentation tasks, with higher Dice Similarity Coefficient (DSC) and mean Intersection over Union (mIoU) scores. Its robustness confirms strong potential for clinical use.

Prostate cancer (PCa) is one of the most common malignancies threatening men's health globally. A comprehensive and integrated approach is essential for its early screening, diagnosis, risk stratification, treatment guidance, and efficacy assessment. Radiomics, leveraging multi-parametric magnetic resonance imaging (mpMRI) and positron emission tomography/computed tomography (PET/CT), has demonstrated significant clinical value in the non-invasive diagnosis, aggressiveness assessment, and prognosis prediction of PCa, with substantial potential when combined with artificial intelligence. In genomics, mutations or deletions in genes such as TMPRSS2-ERG, PTEN, RB1, TP53, and DNA damage repair genes (e.g., BRCA1/2) are closely associated with disease development and progression, holding profound implications for diagnosis, treatment, and prognosis. Concurrently, biomarkers like prostate-specific antigen (PSA), novel urinary markers (e.g., PCA3), and circulating tumor cells (CTCs) are widely utilized in PCa research and management. Integrating these technologies into personalized treatment plans and the broader framework of precision medicine allows for an in-depth exploration of the relationship between specific biomarkers and disease pathogenesis. This review summarizes the current research on radiomics, genomics, and biomarkers in PCa, and discusses their future potential and applications in advancing individualized patient care.