This study introduces MSFE-GallNet-X, a domain-adaptive deep learning model utilizing multi-scale feature extraction (MSFE) to improve the classification accuracy of gallbladder diseases from grayscale ultrasound images, while integrating explainable artificial intelligence (XAI) methods to enhance clinical interpretability. We developed a convolutional neural network-based architecture that automatically learns multi-scale features from a dataset comprising 10,692 high-resolution ultrasound images from 1,782 patients, covering nine gallbladder disease classes, including gallstones, cholecystitis, and carcinoma. The model incorporated Gradient-Weighted Class Activation Mapping (Grad-CAM) and Local Interpretable Model-Agnostic Explanations (LIME) to provide visual interpretability of diagnostic predictions. Model performance was evaluated using standard metrics, including accuracy and F1 score. The MSFE-GallNet-X achieved a classification accuracy of 99.63% and an F1 score of 99.50%, outperforming state-of-the-art models including VGG-19 (98.89%) and DenseNet121 (91.81%), while maintaining greater parameter efficiency, only 1·91 M parameters in gallbladder disease classification. Visualization through Grad-CAM and LIME highlighted critical image regions influencing model predictions, supporting explainability for clinical use. MSFE-GallNet-X demonstrates strong performance on a controlled and balanced dataset, suggesting its potential as an AI-assisted tool for clinical decision-making in gallbladder disease management. Not applicable.

This study aims to develop a noninvasive preoperative predictive model utilizing ultrasound radiomics combined with clinical characteristics to differentiate uterine sarcoma from leiomyoma. This study included 212 patients with uterine mesenchymal lesions (102 sarcomas and 110 leiomyomas). Clinical characteristics were systematically selected through both univariate and multivariate logistic regression analyses. A clinical model was constructed using the selected clinical characteristics. Radiomics features were extracted from transvaginal ultrasound images, and 6 machine learning algorithms were used to construct radiomics models. Then, a clinical radiomics nomogram was developed integrating clinical characteristics with radiomics signature. The effectiveness of these models in predicting uterine sarcoma was thoroughly evaluated. The area under the curve (AUC) was used to compare the predictive efficacy of the different models. The AUC of the clinical model was 0.835 (95% confidence interval [CI]: 0.761-0.883) and 0.791 (95% CI: 0.652-0.869) in the training and testing sets, respectively. The logistic regression model performed best in the radiomics model construction, with AUC values of 0.878 (95% CI: 0.811-0.918) and 0.818 (95% CI: 0.681-0.895) in the training and testing sets, respectively. The clinical radiomics nomogram performed well in differentiation, with AUC values of 0.955 (95% CI: 0.911-0.973) and 0.882 (95% CI: 0.767-0.936) in the training and testing sets, respectively. The clinical radiomics nomogram can provide more comprehensive and personalized diagnostic information, which is highly important for selecting treatment strategies and ultimately improving patient outcomes in the management of uterine mesenchymal tumors.

Despite AI models demonstrating high predictive accuracy for early cholangiocarcinoma(CCA) recurrence, their clinical application faces challenges such as reproducibility, generalizability, hidden biases, and uncertain performance across diverse datasets and populations, raising concerns about their practical applicability. This meta-analysis aims to systematically assess the diagnostic performance of artificial intelligence (AI) models utilizing computed tomography (CT) imaging to predict early recurrence of CCA. A systematic search was conducted in PubMed, Embase, and Web of Science for studies published up to May 2025. Studies were selected based on the PIRTOS framework. Participants (P): Patients diagnosed with CCA (including intrahepatic and extrahepatic locations). Index test (I): AI techniques applied to CT imaging for early recurrence prediction (defined as within 1 year). Reference standard (R): Pathological diagnosis or imaging follow-up confirming recurrence. Target condition (T): Early recurrence of CCA (positive group: recurrence, negative group: no recurrence). Outcomes (O): Sensitivity, specificity, diagnostic odds ratio (DOR), and area under the receiver operating characteristic curve (AUC), assessed in both internal and external validation cohorts. Setting (S): Retrospective or prospective studies using hospital datasets. Methodological quality was assessed using an optimized version of the revised QUADAS-2 tool. Heterogeneity was assessed using the I² statistic. Pooled sensitivity, specificity, DOR and AUC were calculated using a bivariate random-effects model. Nine studies with 30 datasets involving 1,537 patients were included. In internal validation cohorts, CT-based AI models showed a pooled sensitivity of 0.87 (95% CI: 0.81-0.92), specificity of 0.85 (95% CI: 0.79-0.89), DOR of 37.71 (95% CI: 18.35-77.51), and AUC of 0.93 (95% CI: 0.90-0.94). In external validation cohorts, pooled sensitivity was 0.87 (95% CI: 0.81-0.91), specificity was 0.82 (95% CI: 0.77-0.86), DOR was 30.81 (95% CI: 18.79-50.52), and AUC was 0.85 (95% CI: 0.82-0.88). The AUC was significantly lower in external validation cohorts compared to internal validation cohorts (P < .001). Our results show that CT-based AI models predict early CCA recurrence with high performance in internal validation sets and moderate performance in external validation sets. However, the high heterogeneity observed may impact the robustness of these results. Future research should focus on prospective studies and establishing standardized gold standards to further validate the clinical applicability and generalizability of AI models.

Recurrence risk estimation in clear cell renal cell carcinoma (ccRCC) is essential for guiding postoperative surveillance and treatment. The Leibovich score remains widely used for stratifying distant recurrence risk but offers limited patient-level resolution and excludes imaging information. This study evaluates multimodal recurrence prediction by integrating preoperative computed tomography (CT) and postoperative histopathology whole-slide images (WSIs). A modular deep learning framework with pretrained encoders and Cox-based survival modeling was tested across unimodal, late fusion, and intermediate fusion setups. In a real-world ccRCC cohort, WSI-based models consistently outperformed CT-only models, underscoring the prognostic strength of pathology. Intermediate fusion further improved performance, with the best model (TITAN-CONCH with ResNet-18) approaching the adjusted Leibovich score. Random tie-breaking narrowed the gap between the clinical baseline and learned models, suggesting discretization may overstate individualized performance. Using simple embedding concatenation, radiology added value primarily through fusion. These findings demonstrate the feasibility of foundation model-based multimodal integration for personalized ccRCC risk prediction. Future work should explore more expressive fusion strategies, larger multimodal datasets, and general-purpose CT encoders to better match pathology modeling capacity.

Pancreatic cancer is aggressive with high recurrence rates, necessitating accurate prediction models for effective treatment planning, particularly for neoadjuvant chemotherapy or upfront surgery. This study explores the use of variational autoencoder (VAE)-generated synthetic data to predict early tumor recurrence (within six months) in pancreatic cancer patients who underwent upfront surgery. Preoperative data of 158 patients between January 2021 and December 2022 was analyzed, and machine learning models-including Logistic Regression, Random Forest (RF), Gradient Boosting Machine (GBM), and Deep Neural Networks (DNN)-were trained on both original and synthetic datasets. The VAE-generated dataset (n = 94) closely matched the original data (p > 0.05) and enhanced model performance, improving accuracy (GBM: 0.81 to 0.87; RF: 0.84 to 0.87) and sensitivity (GBM: 0.73 to 0.91; RF: 0.82 to 0.91). PET/CT-derived metabolic parameters were the strongest predictors, accounting for 54.7% of the model predictive power with maximum standardized uptake value (SUVmax) showing the highest importance (0.182, 95% CI: 0.165-0.199). This study demonstrates that synthetic data can significantly enhance predictive models for pancreatic cancer recurrence, especially in data-limited scenarios, offering a promising strategy for oncology prediction models.

Automated segmentation of Pancreatic Ductal Adenocarcinoma (PDAC) from MRI is critical for clinical workflows but is hindered by poor tumor-tissue contrast and a scarcity of annotated data. This paper details our submission to the PANTHER challenge, addressing both diagnostic T1-weighted (Task 1) and therapeutic T2-weighted (Task 2) segmentation. Our approach is built upon the nnU-Net framework and leverages a deep, multi-stage cascaded pre-training strategy, starting from a general anatomical foundation model and sequentially fine-tuning on CT pancreatic lesion datasets and the target MRI modalities. Through extensive five-fold cross-validation, we systematically evaluated data augmentation schemes and training schedules. Our analysis revealed a critical trade-off, where aggressive data augmentation produced the highest volumetric accuracy, while default augmentations yielded superior boundary precision (achieving a state-of-the-art MASD of 5.46 mm and HD95 of 17.33 mm for Task 1). For our final submission, we exploited this finding by constructing custom, heterogeneous ensembles of specialist models, essentially creating a mix of experts. This metric-aware ensembling strategy proved highly effective, achieving a top cross-validation Tumor Dice score of 0.661 for Task 1 and 0.523 for Task 2. Our work presents a robust methodology for developing specialized, high-performance models in the context of limited data and complex medical imaging tasks (Team MIC-DKFZ).

Non-invasive and precise identification of clinically significant prostate cancer (csPCa) is essential for the management of prostatic diseases. Our study introduces a novel and interpretable diagnostic method for csPCa, leveraging multi-regional, multiparametric deep learning radiomics based on magnetic resonance imaging (MRI). The prostate regions, including the peripheral zone (PZ) and transition zone (TZ), are automatically segmented using a deep learning framework that combines convolutional neural networks and transformers to generate region-specific masks. Radiomics features are then extracted and selected from multiparametric MRI at the PZ, TZ, and their combined area to develop a multi-regional multiparametric radiomics diagnostic model. Feature contributions are quantified to enhance the model's interpretability and assess the importance of different imaging parameters across various regions. The multi-regional model substantially outperforms single-region models, achieving an optimal area under the curve (AUC) of 0.903 on the internal test set, and an AUC of 0.881 on the external test set. Comparison with other methods demonstrates that our proposed approach exhibits superior performance. Features from diffusion-weighted imaging and apparent diffusion coefficient play a crucial role in csPCa diagnosis, with contribution degrees of 53.28% and 39.52%, respectively. We introduce an interpretable, multi-regional, multiparametric diagnostic model for csPCa using deep learning radiomics. By integrating features from various zones, our model improves diagnostic accuracy and provides clear insights into the key imaging parameters, offering strong potential for clinical applications in csPCa management.

The emergence of new medications for fatty liver conditions has increased the need for reliable and widely available assessment of MRI proton density fat fraction (MRI-PDFF). Whereas low-field MRI presents a promising solution, its utilization is challenging due to the low SNR. This work aims to enhance SNR and enable precise PDFF quantification at low-field MRI using a novel locally low-rank deep learning-based (LLR-DL) reconstruction. LLR-DL alternates between regularized SENSE and a neural network (U-Net) throughout several iterations, operating on complex-valued data. The network processes the spectral projection onto singular value bases, which are computed on local patches across the echoes dimension. The output of the network is recast into the basis of the original echoes and used as a prior for the following iteration. The final echoes are processed by a multi-echo Dixon algorithm. Two different protocols were proposed for imaging at 0.55 T. An iron-and-fat phantom and 10 volunteers were scanned on both 0.55 and 1.5 T systems. Linear regression, t-statistics, and Bland-Altman analyses were conducted. LLR-DL achieved significantly improved image quality compared to the conventional reconstruction technique, with a 32.7% increase in peak SNR and a 25% improvement in structural similarity index. PDFF repeatability was 2.33% in phantoms (0% to 100%) and 0.79% in vivo (3% to 18%), with narrow cross-field strength limits of agreement below 1.67% in phantoms and 1.75% in vivo. An LLR-DL reconstruction was developed and investigated to enable precise PDFF quantification at 0.55 T and improve consistency with 1.5 T results.

Deep learning reconstruction (DLR) offers a variety of advantages over the current standard iterative reconstruction techniques, including decreased image noise without changes in noise texture and less susceptibility to spatial resolution limitations at low dose. These advances may allow for more aggressive dose reduction in CT imaging while maintaining image quality and diagnostic accuracy. However, performance of DLRs is impacted by the type of framework and training data used. In addition, the patient size and clinical task being performed may impact the amount of dose reduction that can be reasonably employed. Multiple DLRs are currently FDA approved with a growing body of literature evaluating performance throughout this body; however, continued work is warranted to evaluate a variety of clinical scenarios to fully explore the evolving potential of DLR. Depending on the type and strength of DLR applied, blurring and occasionally other artifacts may be introduced. DLRs also show promise in artifact reduction, particularly metal artifact reduction. This commentary focuses primarily on current DLR data for abdominal applications, current challenges, and future areas of potential exploration.

Treatment resistance prevents patients with preoperative chemoradiotherapy or targeted radiolabeled immunotherapy from achieving a good result, which remains a major challenge in the prostate cancer (PCa) area. A novel integrative framework combining a machine learning workflow with proteogenomic profiling was used to identify predictive ultrasound biomarkers and classify patient response to radiolabeled immunotherapy in high-risk PCa patients who are treatment resistant. The deep stacked autoencoder (DSAE) model, combined with Extreme Gradient Boosting, was designed for feature refinement and classification. The Cancer Genome Atlas and an independent radiotherapy-treated cohort have been utilized to collect multiomics data through their respective applications. In addition to genetic mutations (whole-exome sequencing), these data contained proteomic (mass spectrometry) and transcriptomic (RNA sequencing) data. Maintaining biological variety across omics layers while reducing the dimensionality of the data requires the use of the DSAE architecture. Resistance phenotypes show a notable relationship with proteogenomic profiles, including DNA repair pathways (Breast Cancer gene 2 [BRCA2], ataxia-telangiectasia mutated [ATM]), androgen receptor (AR) signaling regulators, and metabolic enzymes (ATP citrate lyase [ACLY], isocitrate dehydrogenase 1 [IDH1]). A specific panel of ultrasound biomarkers has been confirmed in a state deemed preclinical using patient-derived xenografts. To support clinical translation, real-time phenotypic features from ultrasound imaging (e.g., perfusion, stiffness) were also considered, providing complementary insights into the tumor microenvironment and treatment responsiveness. This approach provides an integrated platform that offers a clinically actionable foundation for the development of radiolabeled immunotherapy drugs before surgical operations.