Reliable out-of-distribution (OOD) detection is important for safe deployment of deep learning models in fetal ultrasound amidst heterogeneous image characteristics and clinical settings. OOD detection relies on estimating a classification model's uncertainty, which should increase for OOD samples. While existing research has largely focused on uncertainty quantification methods, this work investigates the impact of the classification task itself. Through experiments with eight uncertainty quantification methods across four classification tasks, we demonstrate that OOD detection performance significantly varies with the task, and that the best task depends on the defined ID-OOD criteria; specifically, whether the OOD sample is due to: i) an image characteristic shift or ii) an anatomical feature shift. Furthermore, we reveal that superior OOD detection does not guarantee optimal abstained prediction, underscoring the necessity to align task selection and uncertainty strategies with the specific downstream application in medical image analysis.

To investigate MRI-based habitat analysis for its value in predicting pathologic response following neoadjuvant chemoradiotherapy (nCRT) in rectal cancer (RC) patients. 1021 RC patients in three hospitals were divided into the training and test sets (n = 319), the internal validation set (n = 317), and external validation sets 1 (n = 158) and 2 (n = 227). Deep learning was performed to automatically segment the entire lesion on high-resolution MRI. Simple linear iterative clustering was used to divide each tumor into subregions, from which radiomics features were extracted. The optimal number of clusters reflecting the diversity of the tumor ecosystem was determined. Finally, four models were developed: clinical, intratumoral heterogeneity (ITH)-based, radiomics, and fusion models. The performance of these models was evaluated. The impact of nCRT on disease-free survival (DFS) was further analyzed. The Delong test revealed the fusion model (AUCs of 0.867, 0.851, 0.852, and 0.818 in the four cohorts, respectively), the radiomics model (0.831, 0.694, 0.753, and 0.705, respectively), and the ITH model (0.790, 0.786, 0.759, and 0.722, respectively) were all superior to the clinical model (0.790, 0.605, 0.735, and 0.704, respectively). However, no significant differences were detected between the fusion and ITH models. Patients stratified using the fusion model showed significant differences in DFS between the good and poor response groups (all p < 0.05 in the four sets). The fusion model combining clinical factors, radiomics features, and ITH features may help predict pathologic response in RC cases receiving nCRT. Question Identifying rectal cancer (RC) patients likely to benefit from neoadjuvant chemoradiotherapy (nCRT) before treatment is crucial. Findings The fusion model shows the best performance in predicting response after neoadjuvant chemoradiotherapy. Clinical relevance The fusion model integrates clinical characteristics, radiomics features, and intratumoral heterogeneity (ITH)features, which can be applied for the prediction of response to nCRT in RC patients, offering potential benefits in terms of personalized treatment strategies.

Constructing a multi-task global decision support system based on preoperative enhanced CT features to predict the mismatch repair (MMR) status, T stage, and pathological risk factors (e.g., histological differentiation, lymphovascular invasion) for patients with non-metastatic colon cancer. 372 eligible non-metastatic colon cancer (NMCC) participants (training cohort: n = 260; testing cohort: n = 112) were enrolled from two institutions. The 34 features (imaging features: n = 27; clinical features: n = 7) were subjected to feature selection using LASSO, Boruta, ReliefF, mRMR, and XGBoost-RFE, respectively. In each of the three categories-MMR, pT staging, and pathological risk factors-four features were selected to construct the total feature set. Subsequently, the multitask model was built with 14 machine learning algorithms. The predictive performance of the machine model was evaluated using the area under the receiver operating characteristic curve (AUC). The final feature set for constructing the model is based on the mRMR feature screening method. For the final MMR classification, pT staging, and pathological risk factors, SVC, Bernoulli NB, and Decision Tree algorithm were selected respectively, with AUC scores of 0.80 [95% CI 0.71-0.89], 0.82 [95% CI 0.71-0.94], and 0.85 [95% CI 0.77-0.93] on the test set. Furthermore, a direct multiclass model constructed using the total feature set resulted in an average AUC of 0.77 across four management plans in the test set. The multi-task machine learning model proposed in this study enables non-invasive and precise preoperative stratification of patients with NMCC based on MMR status, pT stage, and pathological risk factors. This predictive tool demonstrates significant potential in facilitating preoperative risk stratification and guiding individualized therapeutic strategies.

In image-guided radiation therapy (IGRT), deformable image registration between computed tomography (CT) and cone beam computed tomography (CBCT) images remain challenging due to the computational cost of iterative algorithms and the data dependence of supervised deep learning methods. Implicit neural representation (INR) provides a promising alternative, but conventional multilayer perceptron (MLP) might struggle to efficiently represent complex, nonlinear deformations. This study introduces a novel INR-based registration framework that models the deformation as a continuous, time-varying velocity field, parameterized by a Kolmogorov-Arnold Network (KAN) constructed using Jacobi polynomials. To our knowledge, this is the first integration of KAN into medical image registration, establishing a new paradigm beyond standard MLP-based INR. For improved efficiency, the KAN estimates low-dimensional principal components of the velocity field, which are reconstructed via inverse principal component analysis and temporally integrated to derive the final deformation. This approach achieves a ~70% improvement in computational efficiency relative to direct velocity field modeling while ensuring smooth and topology-preserving transformations through velocity regularization. Evaluation on a publicly available pelvic CT-CBCT dataset demonstrates up to 6% improvement in registration accuracy over traditional iterative methods and ~3% over MLP-based INR baselines, indicating the potential of the proposed method as an efficient and generalizable alternative for deformable registration.

This systematic review evaluates advanced ultrasound quantitative techniques including contrast-enhanced ultrasound, elastography, quantitative ultrasound (QUS), multiparametric ultrasound, and artificial intelligence for characterizing focal liver lesions (FLLs). It critically appraises their technical principles, parameter extraction methodologies, and clinical validation frameworks. It further integrates and comparatively analyzes their diagnostic performance across major FLL subtypes, including hepatocellular carcinoma, metastases, hemangioma, and focal nodular hyperplasia. This work provides a foundation for improving noninvasive FLL diagnosis and highlights the imperative for standardization and clinical translation of advanced QUS in hepatology.

Fatty liver disease, encompassing non-alcoholic fatty liver disease (NAFLD) and alcohol-related liver disease (ALD), affects ∼25% of adults globally. Magnetic resonance imaging (MRI), particularly proton density fat fraction (PDFF), is the non-invasive gold standard for hepatic steatosis quantification, but its clinical use is limited by cost, protocol variability, a analysis time. Machine learning (ML) and deep learning (DL) techniques, including convolutional neural networks (CNNs) and generative adversarial networks (GANs), show promise in enhancing MRI-based quantification and staging. To systematically review the diagnostic accuracy, reproducibility, and clinical utility of ML and DL techniques applied to MRI for quantifying and staging hepatic steatosis in fatty liver disease. This systematic review was registered in PROSPERO (CRD420251121056) and adhered to PRISMA guidelines, searching PubMed, Cochrane Library, Scopus, IEEE Xplore, Web of Science, Google Scholar, and grey literature for studies on ML/DL applications in MRI for fatty liver disease. Eligible studies involved human participants with suspected/confirmed NAFLD, NASH, or ALD, using ML/DL (e.g., CNNs, GANs) on MRI data (e.g., PDFF, Dixon MRI). Outcomes included diagnostic accuracy (sensitivity, specificity, area under the curve (AUC)), reproducibility (intraclass correlation coefficient (ICC), Dice), and clinical utility (e.g., treatment planning). Two reviewers screened studies, extracted data, and assessed risk of bias using QUADAS-2. Narrative synthesis and meta-analysis (where feasible) were conducted. From 2550 records, 15 studies (n = 25-1038) were included, using CNNs, GANs, radiomics, and dictionary learning on PDFF, chemical shift-encoded MRI, or Dixon MRI. Diagnostic accuracy was high (AUC 0.85-0.97, r = 0.94-0.99 vs. biopsy/MRS), with reproducibility metrics robust (ICC 0.94-0.99, Dice 0.87-0.94). Efficiency improved significantly (e.g., processing <0.16 s/slice, scan time <1 min). Clinical utility included virtual biopsies, surgical planning, and treatment monitoring. Limitations included small sample sizes, single-center designs, and vendor variability. ML and DL enhance MRI-based hepatic steatosis assessment, offering high accuracy, reproducibility, and efficiency. CNNs excel in segmentation/PDFF quantification, while GANs and radiomics aid free-breathing MRI and NASH staging. Multi-center studies and standardization are needed for clinical integration.

We conducted a systematic review and meta-analysis to assess and compare the diagnostic performance of prostate-specific membrane antigen positron-emission tomography (PSMA PET) with conventional imaging modalities in detecting biochemical recurrence of prostate cancer, and to assess the role of artificial intelligence in this context. A comprehensive search of PubMed, Embase, Web of Science, the Cochrane Library, and Scopus was conducted for studies, initially on May 7, 2025, and updated on July 28, 2025. Studies that compared PSMA PET with conventional imaging and assessed artificial intelligence for detecting biochemical recurrence of prostate cancer were considered. The QUADAS-2 technique was employed to evaluate study quality. Diagnosis accuracy and detection rates were aggregated utilizing a bivariate random-effects model. A total of 7637 patients from 67 studies were included. PSMA PET demonstrated significantly higher overall diagnostic accuracy for biochemical recurrence of prostate cancer compared to mpMRI, CT, and AI test sets, with accuracy values of (0.89 vs. 0.71, 0.45, and 0.76, P<0.01). For local recurrence, mpMRI outperformed PSMA PET and CT (0.93 vs. 0.84 and 0.77, P<0.01). PSMA PET was superior in detecting lymph node metastasis than mpMRI and CT (0.89 vs. 0.79 and 0.72, P<0.05). For bone metastasis, PSMA PET outperformed mpMRI, CT, and Bone scan (0.95 vs. 0.85, 0.81, and 0.80, P<0.05). For visceral metastasis, PSMA PET outperformed mpMRI (0.96 vs. 0.89, P=0.23), and CT (0.96 vs. 0.78, P<0.05). 21 studies involving 3113 samples were included to evaluate the performance of artificial intelligence in detecting biochemical recurrence of prostate cancer. The pooled sensitivity, specificity, DOR, and AUC of AI test sets in detecting biochemical recurrence of prostate cancer were 0.77, 0.76, 10.39, and 0.79. Heterogeneity limits the generalizability of our findings. PSMA PET outperformed mpMRI and CT in detecting overall, local recurrence, bone, and visceral metastasis, while mpMRI was more effective for local recurrence. While AI exhibits potential diagnostic efficacy. Despite promising results, heterogeneity and limited validation highlight the need for further research to support routine clinical use.

Micro-ultrasound (micro-US) is a promising imaging technique for cancer detection and computer-assisted visualization. This study investigates prostate capsule segmentation using deep learning techniques from micro-US images, addressing the challenges posed by the ambiguous boundaries of the prostate capsule. Existing methods often struggle in such cases, motivating the development of a tailored approach. This study introduces an adaptive focal loss function that dynamically emphasizes both hard and easy regions, taking into account their respective difficulty levels and annotation variability. The proposed methodology has two primary strategies: integrating a standard focal loss function as a baseline to design an adaptive focal loss function for proper prostate capsule segmentation. The focal loss baseline provides a robust foundation, incorporating class balancing and focusing on examples that are difficult to classify. The adaptive focal loss offers additional flexibility, addressing the fuzzy region of the prostate capsule and annotation variability by dilating the hard regions identified through discrepancies between expert and non-expert annotations. The proposed method dynamically adjusts the segmentation model's weights better to identify the fuzzy regions of the prostate capsule. The proposed adaptive focal loss function demonstrates superior performance, achieving a mean dice coefficient (DSC) of 0.940 and a mean Hausdorff distance (HD) of 1.949 mm in the testing dataset. These results highlight the effectiveness of integrating advanced loss functions and adaptive techniques into deep learning models. This enhances the accuracy of prostate capsule segmentation in micro-US images, offering the potential to improve clinical decision-making in prostate cancer diagnosis and treatment planning.

One in 25 deaths worldwide is related to liver disease, and often with multiple hepatosplenic conditions. Yet, little is understood of the risk factors for hepatosplenic multimorbidity, especially in the context of chronic infections. We present a novel Bayesian multitask learning framework to jointly model 45 hepatosplenic conditions assessed using point-of-care B-mode ultrasound for 3155 individuals aged 5-91 years within the SchistoTrack cohort across rural Uganda where chronic intestinal schistosomiasis is endemic. We identified distinct and shared biomedical, socioeconomic, and spatial risk factors for individual conditions and hepatosplenic multimorbidity, and introduced methods for measuring condition dependencies as risk factors. Notably, for gastro-oesophageal varices, we discovered key risk factors of older age, lower hemoglobin concentration, and severe schistosomal liver fibrosis. Our findings provide a compendium of risk factors to inform surveillance, triage, and follow-up, while our model enables improved prediction of hepatosplenic multimorbidity, and if validated on other systems, general multimorbidity.

Neoadjuvant chemoimmunotherapy (NACI) regimen (camrelizumab plus paclitaxel and nedaplatin) has shown promising potential in patients with esophageal squamous cell carcinoma (ESCC), but accurately predicting the therapeutic response remains a challenge. To develop and validate a CT-based machine learning model that incorporates both intratumoral and peritumoral heterogeneity for predicting the pathological response of ESCC patients after NACI. Patients with ESCC who underwent surgery following NACI between June 2020 and July 2024 were included retrospectively and prospectively. Univariate and multivariate logistic regression analyses were performed to identify clinical variables associated with pathological response. Traditional radiomics features and habitat radiomics features from the intratumoral and peritumoral regions were extracted from post-treatment CT images, and six predictive models were established using 14 machine learning algorithms. The combined model was developed by integrating intratumoral and peritumoral habitat radiomics features with clinical variables. The performance of the models was evaluated using the area under the receiver operating characteristic curve (AUC). A total of 157 patients (mean [SD] age, 59.6 [6.5] years) were enrolled in our study, of whom 60 (38.2%) achieved major pathological response (MPR) and 40 (25.5%) achieved pathological complete response (pCR). The combined model demonstrated excellent predictive ability for MPR after NACI, with an AUC of 0.915 (95% CI, 0.844-0.981), accuracy of 0.872, sensitivity of 0.733, and specificity of 0.938 in the test set. In sensitivity analysis focusing on pCR, the combined model exhibited robust performance, with an AUC of 0.895 (95% CI, 0.782-0.980) in the test set. The combined model integrating intratumoral and peritumoral habitat radiomics features with clinical variables can accurately predict MPR in ESCC patients after NACI and shows promising potential in predicting pCR.