Early detection through screening is critical for reducing gastric cancer (GC) mortality. However, in most high-prevalence regions, large-scale screening remains challenging due to limited resources, low compliance and suboptimal detection rate of upper endoscopic screening. Therefore, there is an urgent need for more efficient screening protocols. Noncontrast computed tomography (CT), routinely performed for clinical purposes, presents a promising avenue for large-scale designed or opportunistic screening. Here we developed the Gastric Cancer Risk Assessment Procedure with Artificial Intelligence (GRAPE), leveraging noncontrast CT and deep learning to identify GC. Our study comprised three phases. First, we developed GRAPE using a cohort from 2 centers in China (3,470 GC and 3,250 non-GC cases) and validated its performance on an internal validation set (1,298 cases, area under curve = 0.970) and an independent external cohort from 16 centers (18,160 cases, area under curve = 0.927). Subgroup analysis showed that the detection rate of GRAPE increased with advancing T stage but was independent of tumor location. Next, we compared the interpretations of GRAPE with those of radiologists and assessed its potential in assisting diagnostic interpretation. Reader studies demonstrated that GRAPE significantly outperformed radiologists, improving sensitivity by 21.8% and specificity by 14.0%, particularly in early-stage GC. Finally, we evaluated GRAPE in real-world opportunistic screening using 78,593 consecutive noncontrast CT scans from a comprehensive cancer center and 2 independent regional hospitals. GRAPE identified persons at high risk with GC detection rates of 24.5% and 17.7% in 2 regional hospitals, with 23.2% and 26.8% of detected cases in T1/T2 stage. Additionally, GRAPE detected GC cases that radiologists had initially missed, enabling earlier diagnosis of GC during follow-up for other diseases. In conclusion, GRAPE demonstrates strong potential for large-scale GC screening, offering a feasible and effective approach for early detection. ClinicalTrials.gov registration: NCT06614179 .

This study compared the capabilities of two-dimensional (2D) and three-dimensional (3D) deep learning (DL), radiomics, and fusion models to predict postpartum hemorrhage (PPH), using sagittal T2-weighted MRI images. This retrospective study successively included 581 pregnant women suspected of placenta accreta spectrum (PAS) disorders who underwent placental MRI assessment between May 2018 and June 2024 in two hospitals. Clinical information was collected, and MRI images were analyzed by two experienced radiologists. The study cohort was divided into training (hospital 1, n=470) and validation (hospital 2, n=160) sets. Radiomics features were extracted after image segmentation to develop the radiomics model, 2D and 3D DL models were developed, and two fusion strategies (early and late fusion) were used to construct the fusion models. ROC curves, AUC, sensitivity, specificity, calibration curves, and decision curve analysis were used to evaluate the models' performance. The late-fusion model (DLRad_LF) yielded the highest performance, with AUCs of 0.955 (95% CI: 0.935-0.974) and 0.898 (95% CI: 0.848-0.949) in the training and validation sets, respectively. In the validation set, the AUC of the 3D DL model was significantly larger than those of the radiomics (AUC=0.676, P<0.001) and 2D DL (AUC=0.752, P<0.001) models. Subgroup analysis found that placenta previa and PAS did not impact the models' performance significantly. The DLRad_LF model could predict PPH reasonably accurately based on sagittal T2-weighted MRI images.

This study aimed to develop a deep learning (DL) model based on three-dimensional multi-parametric magnetic resonance imaging (mpMRI) for preoperative assessment of lymph node metastasis (LNM) in rectal cancer (RC) and to investigate the contribution of different MRI sequences. A total of 613 eligible patients with RC from four medical centres who underwent preoperative mpMRI were retrospectively enrolled and randomly assigned to training (n = 372), validation (n = 106), internal test (n = 88) and external test (n = 47) cohorts. A multi-parametric multi-scale EfficientNet (MMENet) was designed to effectively extract LNM-related features from mpMR for preoperative LNM assessment. Its performance was compared with other DL models and radiologists using metrics of area under the receiver operating curve (AUC), accuracy (ACC), sensitivity, specificity and average precision with 95% confidence interval (CI). To investigate the utility of various MRI sequences, the performances of the mono-parametric model and the MMENet with different sequences combinations as input were compared. The MMENet using a combination of T2WI, DWI and DCE sequence achieved an AUC of 0.808 (95% CI 0.720-0.897) with an ACC of 71.6% (95% CI 62.3-81.0) in the internal test cohort and an AUC of 0.782 (95% CI 0.636-0.925) with an ACC of 76.6% (95% CI 64.6-88.6) in the external test cohort, outperforming the mono-parametric model, the MMENet with other sequences combinations and the radiologists. The MMENet, leveraging a combination of T2WI, DWI and DCE sequences, can accurately assess LNM in RC preoperatively and holds great promise for automated evaluation of LNM in clinical practice.

To evaluate PANCANAI, a previously developed AI model for pancreatic cancer (PC) detection, on a longitudinal cohort of patients. In particular, aiming for PC detection on scans acquired before histopathologic diagnosis was assessed. The model has been previously trained to predict PC suspicion on 2134 portal venous CTs. In this study, the algorithm was evaluated on a retrospective cohort of Danish patients with biopsy-confirmed PC and with CT scans acquired between 2006 and 2016. The sensitivity was measured, and bootstrapping was performed to provide median and 95% CI. The study included 1083 PC patients (mean age: 69 y ± 11, 575 men). CT scans were divided into 2 groups: (1) concurrent diagnosis (CD): 1022 CT scans acquired within 2 months around histopathologic diagnosis, and (2) prediagnosis (PD): 198 CT scans acquired before histopathologic diagnosis (median 7 months before diagnosis). The sensitivity was 91.8% (938 of 1022; 95% CI: 89.9-93.5) and 68.7% (137 of 198; 95% CI: 62.1-75.3) on the CD and PD groups, respectively. Sensitivity on CT scans acquired 1 year or more before diagnosis was 53.9% (36 of 67; 95% CI: 41.8-65.7). Sensitivity on CT scans acquired at stage I was 82.9% (29 of 35; 95% CI: 68.6-94.3). PANCANAI showed high sensitivity for automatic PC detection on a large retrospective cohort of biopsy-confirmed patients. PC suspicion was detected in more than half of the CT scans that were acquired at least a year before histopathologic diagnosis.

The clinical utility of the DeepSeek-V3 (DSV3) model in enhancing the accuracy of Liver Imaging Reporting and Data System (LI-RADS, LR) classification remains underexplored. This study aimed to evaluate the diagnostic performance of DSV3 in LR classifications compared to radiologists with varying levels of experience and to assess its potential as a decision-support tool in clinical practice. A dual-phase retrospective-prospective study analyzed 426 liver lesions (300 retrospective, 126 prospective) in high-risk HCC patients who underwent Magnetic Resonance Imaging (MRI) or Computed Tomography (CT). Three radiologists (one junior, two seniors) independently classified lesions using LR v2018 criteria, while DSV3 analyzed unstructured radiology reports to generate corresponding classifications. In the prospective cohort, DSV3 processed inputs in both Chinese and English to evaluate language impact. Performance was compared using chi-square test or Fisher's exact test, with pathology as the gold standard. In the retrospective cohort, DSV3 significantly outperformed junior radiologists in diagnostically challenging categories: LR-3 (17.8% vs. 39.7%, p<0.05), LR-4 (80.4% vs. 46.2%, p<0.05), and LR-5 (86.2% vs. 66.7%, p<0.05), while showing comparable accuracy in LR-1 (90.8% vs. 88.7%), LR-2 (11.9% vs. 25.6%), and LR-M (79.5% vs. 62.1%) classifications (all p>0.05). Prospective validation confirmed these findings, with DSV3 demonstrating superior performance for LR-3 (13.3% vs. 60.0%), LR-4 (93.3% vs. 66.7%), and LR-5 (93.5% vs. 67.7%) compared to junior radiologists (all p<0.05). Notably, DSV3 achieved diagnostic parity with senior radiologists across all categories (p>0.05) and maintained consistent performance between Chinese and English inputs. The DSV3 model effectively improves diagnostic accuracy of LR-3 to LR-5 classifications among junior radiologists . Its language-independent performance and ability to match senior-level expertise suggest strong potential for clinical implementation to standardize HCC diagnosis and optimize treatment decisions.

The International Society of Urological Pathology (ISUP) grading of prostate cancer (PCa) is a crucial factor in the management and treatment planning for PCa patients. An accurate and non-invasive assessment of the ISUP grading group could significantly improve biopsy decisions and treatment planning. The use of PSMA-PET/CT radiomics for predicting ISUP has not been widely studied. The aim of this study is to investigate the role of ⁶⁸Ga-PSMA PET/CT radiomics in predicting the ISUP grading of primary PCa. This study included 415 PCa patients who underwent ⁶⁸Ga-PSMA PET/CT scans before prostate biopsy or radical prostatectomy. Patients were from three centers: Xiangya Hospital, Central South University (252 cases), Qilu Hospital of Shandong University (External Validation 1, 108 cases), and Qingdao University Medical College (External Validation 2, 55 cases). Xiangya Hospital cases were split into training and testing groups (1:1 ratio), with the other centers serving as external validation groups. Feature selection was performed using Minimum Redundancy Maximum Relevance (mRMR) and Least Absolute Shrinkage and Selection Operator (LASSO) algorithms. Eight machine learning classifiers were trained and tested with ten-fold cross-validation. Sensitivity, specificity, and AUC were calculated for each model. Additionally, we combined the radiomic features with maximum Standardized Uptake Value (SUVmax) and prostate-specific antigen (PSA) to create prediction models and tested the corresponding performances. The best-performing model in the Xiangya Hospital training cohort achieved an AUC of 0.868 (sensitivity 72.7%, specificity 96.0%). Similar trends were seen in the testing cohort and external validation centers (AUCs: 0.860, 0.827, and 0.812). After incorporating PSA and SUVmax, a more robust model was developed, achieving an AUC of 0.892 (sensitivity 77.9%, specificity 96.0%) in the training group. This study established and validated a radiomics model based on ⁶⁸Ga-PSMA PET/CT, offering an accurate, non-invasive method for predicting ISUP grades in prostate cancer. A multicenter design with external validation ensured the model's robustness and broad applicability. This is the largest study to date on PSMA radiomics for predicting ISUP grades. Notably, integrating SUVmax and PSA metrics with radiomic features significantly improved prediction accuracy, providing new insights and tools for personalized diagnosis and treatment.

Artificial intelligence (AI) is a disruptive technology that is currently finding widespread application after having long been confined to the domain of specialists. In urology, in particular, new fields of application are continuously emerging, which are being studied both in preclinical basic research and in clinical applications. Potential applications include image recognition in the operating room or interpreting images from radiology and pathology, the automatic measurement of urinary stones and radiotherapy. Certain medical devices, particularly in the field of AI-based predictive biomarkers, have already been incorporated into international guidelines. In addition, AI is playing an increasingly more important role in administrative tasks and is expected to lead to enormous changes, especially in the outpatient sector. For urologists, it is becoming increasingly more important to engage with this technology, to pursue appropriate training and therefore to optimally implement AI into the treatment of patients and in the management of their practices or hospitals.

Bias field artifacts in magnetic resonance imaging (MRI) scans introduce spatially smooth intensity inhomogeneities that degrade image quality and hinder downstream analysis. To address this challenge, we propose a novel variational Hadamard U-Net (VHU-Net) for effective body MRI bias field correction. The encoder comprises multiple convolutional Hadamard transform blocks (ConvHTBlocks), each integrating convolutional layers with a Hadamard transform (HT) layer. Specifically, the HT layer performs channel-wise frequency decomposition to isolate low-frequency components, while a subsequent scaling layer and semi-soft thresholding mechanism suppress redundant high-frequency noise. To compensate for the HT layer's inability to model inter-channel dependencies, the decoder incorporates an inverse HT-reconstructed transformer block, enabling global, frequency-aware attention for the recovery of spatially consistent bias fields. The stacked decoder ConvHTBlocks further enhance the capacity to reconstruct the underlying ground-truth bias field. Building on the principles of variational inference, we formulate a new evidence lower bound (ELBO) as the training objective, promoting sparsity in the latent space while ensuring accurate bias field estimation. Comprehensive experiments on abdominal and prostate MRI datasets demonstrate the superiority of VHU-Net over existing state-of-the-art methods in terms of intensity uniformity, signal fidelity, and tissue contrast. Moreover, the corrected images yield substantial downstream improvements in segmentation accuracy. Our framework offers computational efficiency, interpretability, and robust performance across multi-center datasets, making it suitable for clinical deployment.

Endoscopic image classification plays a pivotal role in medical diagnostics by identifying anatomical landmarks and pathological findings. However, conventional closed-set classification frameworks are inherently limited in open-world clinical settings, where previously unseen conditions can arise andcompromise model reliability. To address this, we explore the application of Open Set Recognition (OSR) techniques on the Kvasir dataset, a publicly available and diverse endoscopic image collection. In this study, we evaluate and compare the OSR capabilities of several representative deep learning architectures, including ResNet-50, Swin Transformer, and a hybrid ResNet-Transformer model, under both closed-set and open-set conditions. OpenMax is adopted as a baseline OSR method to assess the ability of these models to distinguish known classes from previously unseen categories. This work represents one of the first efforts to apply open set recognition to the Kvasir dataset and provides a foundational benchmark for evaluating OSR performance in medical image analysis. Our results offer practical insights into model behavior in clinically realistic settings and highlight the importance of OSR techniques for the safe deployment of AI systems in endoscopy.

This study aimed to develop and validate a deep learning radiomics nomogram (DLRN) derived from ultrasound images to improve predictive accuracy for lymph node metastasis (LNM) in pancreatic cancer (PC) patients. A retrospective analysis of 249 histopathologically confirmed PC cases, including 78 with LNM, was conducted, with an 8:2 division into training and testing cohorts. Eight transfer learning models and a baseline logistic regression model incorporating handcrafted radiomic and clinicopathological features were developed to evaluate predictive performance. Diagnostic effectiveness was assessed for junior and senior ultrasound physicians, both with and without DLRN assistance. InceptionV3 showed the highest performance among DL models (AUC = 0.844), while the DLRN model, integrating deep learning and radiomic features, demonstrated superior accuracy (AUC = 0.909), robust calibration, and significant clinical utility per decision curve analysis. DLRN assistance notably enhanced diagnostic performance, with AUC improvements of 0.238 (<i>p</i> = 0.006) for junior and 0.152 (<i>p</i> = 0.085) for senior physicians. The ultrasound-based DLRN model exhibits strong predictive capability for LNM in PC, offering a valuable decision-support tool that bolsters diagnostic accuracy, especially among less experienced clinicians, thereby supporting more tailored therapeutic strategies for PC patients.