Domain shift has been shown to have a major detrimental effect on AI model performance however prior studies on domain shift for MRI prostate cancer segmentation have been limited to small, or heterogenous cohorts. Our objective was to assess whether prostate cancer segmentation models trained on local MRI data continue to outperform those trained on external data with cohorts exceeding 1000. We simulated a multi-institutional consortium using the public PICAI dataset (PICAI-TRAIN: <i>1241 exams</i>, PICAI-TEST: <i>259</i>) and a local dataset (LOCAL-TRAIN: <i>1400 exams</i>, LOCAL-TEST: <i>308</i>). IRB approval was obtained and consent waived. We compared nnUNet-v2 models trained on the combined data (CENTRAL-TRAIN) and separately on PICAI-TRAIN and LOCAL-TRAIN. Accuracy was evaluated using the open source PICAI Score on LOCAL-TEST. Significance was tested using bootstrapping. Just 22% (309/1400) of LOCAL-TRAIN exams would be sufficient to match the performance of a model trained on PICAI-TRAIN. The CENTRAL-TRAIN performance was similar to LOCAL-TRAIN performance, with PICAI Scores [95% CI] of 65 [58-71] and 66 [60-72], respectively. Both of these models exceeded the model trained on PICAI-TRAIN alone which had a score of 58 [51-64] (<i>P</i> < .002). Reducing training set size did not alter these relative trends. Domain shift limits MRI prostate cancer segmentation performance even when training with over 1000 exams from 3 external institutions. Use of local data is paramount at these scales.

Pathologic schizophrenia processes originate early in brain development, leading to detectable brain alterations via structural and functional magnetic resonance imaging (MRI). Recent MRI studies have sought to characterize disease effects from a brain age perspective, but developmental deviations from the typical brain age trajectory in youths with schizophrenia remain unestablished. This study investigated brain development deviations in early-onset schizophrenia (EOS) patients by applying machine learning algorithms to structural and functional MRI data. Multimodal MRI data, including T1-weighted MRI (T1w-MRI), diffusion MRI, and resting-state functional MRI (rs-fMRI) data, were collected from 80 antipsychotic-naive first-episode EOS patients and 91 typically developing (TD) controls. The morphometric similarity connectome (MSC), structural connectome (SC), and functional connectome (FC) were separately constructed by using these three modalities. According to these connectivity features, eight brain age estimation models were first trained with the TD group, the best of which was then used to predict brain ages in patients. Individual brain age gaps were assessed as brain ages minus chronological ages. Both the SC and MSC features performed well in brain age estimation, whereas the FC features did not. Compared with the TD controls, the EOS patients showed increased absolute brain age gaps when using the SC or MSC features, with opposite trends between childhood and adolescence. These increased brain age gaps for EOS patients were positively correlated with the severity of their clinical symptoms. These findings from a multimodal brain age perspective suggest that advanced brain age gaps exist early in youths with schizophrenia.

Robust 3D brain tumor MRI segmentation is significant for diagnosis and treatment. However, the tumor heterogeneity, irregular shape, and complicated texture are challenging. Deep learning has transformed medical image analysis by feature extraction directly from the data, greatly enhancing the accuracy of segmentation. The functionality of deep models can be complemented by adding modules like texture-sensitive customized convolution layers and attention mechanisms. These components allow the model to focus its attention on pertinent locations and boundary definition problems. In this paper, a texture-aware deep learning method that improves the U-Net structure by adding a trainable Gabor convolution layer in the input for rich textural feature capture is proposed. Such features are fused in parallel with standard convolutional outputs to better represent tumors. The model also utilizes dual attention modules, Squeeze-and-Excitation blocks in the encoder for dynamically adjusting channel-wise features and Attention Gates for boosting skip connections by removing trivial areas and weighting tumor areas. The working of each module is explored through explainable artificial intelligence methods to ensure interpretability. To address class imbalance, a weighted combined loss function is applied. The model achieves Dice coefficients of 91.62%, 89.92%, and 88.86% for whole tumor, tumor core, and enhancing tumor respectively on BraTS2021 dataset. Large-scale quantitative and qualitative evaluations on BraTS2021, validated on BraTS benchmarks, prove the accuracy and robustness of the proposed model. The proposed approach results are superior to benchmark U-Net and other state-of-the-art segmentation methods, offering a robust and interpretable solution for clinical use.

Predicting the spatio-temporal progression of brain tumors is essential for guiding clinical decisions in neuro-oncology. We propose a hybrid mechanistic learning framework that combines a mathematical tumor growth model with a guided denoising diffusion implicit model (DDIM) to synthesize anatomically feasible future MRIs from preceding scans. The mechanistic model, formulated as a system of ordinary differential equations, captures temporal tumor dynamics including radiotherapy effects and estimates future tumor burden. These estimates condition a gradient-guided DDIM, enabling image synthesis that aligns with both predicted growth and patient anatomy. We train our model on the BraTS adult and pediatric glioma datasets and evaluate on 60 axial slices of in-house longitudinal pediatric diffuse midline glioma (DMG) cases. Our framework generates realistic follow-up scans based on spatial similarity metrics. It also introduces tumor growth probability maps, which capture both clinically relevant extent and directionality of tumor growth as shown by 95th percentile Hausdorff Distance. The method enables biologically informed image generation in data-limited scenarios, offering generative-space-time predictions that account for mechanistic priors.

Automatic quantification of intramyocardial motion and strain from tagging MRI remains an important but challenging task. We propose a method using implicit neural representations (INRs), conditioned on learned latent codes, to predict continuous left ventricular (LV) displacement -- without requiring inference-time optimisation. Evaluated on 452 UK Biobank test cases, our method achieved the best tracking accuracy (2.14 mm RMSE) and the lowest combined error in global circumferential (2.86%) and radial (6.42%) strain compared to three deep learning baselines. In addition, our method is $\sim$380$\times$ faster than the most accurate baseline. These results highlight the suitability of INR-based models for accurate and scalable analysis of myocardial strain in large CMR datasets.

Effective preoperative planning requires accurate algorithms for segmenting anatomical structures across diverse datasets, but traditional models struggle with generalization. This study presents a novel machine learning methodology to improve algorithm generalization for 3D anatomical reconstruction beyond breast cancer applications. We processed 120 retrospective breast MRIs (January 2018-June 2023) through three phases: anonymization and manual segmentation of T1-weighted and dynamic contrast-enhanced sequences; co-registration and segmentation of whole breast, fibroglandular tissue, and tumors; and 3D visualization using ITK-SNAP. A human-in-the-loop approach refined segmentations using U-Mamba, designed to generalize across imaging scenarios. Dice similarity coefficient assessed overlap between automated segmentation and ground truth. Clinical relevance was evaluated through clinician and patient interviews. U-Mamba showed strong performance with DSC values of 0.97 ($\pm$0.013) for whole organs, 0.96 ($\pm$0.024) for fibroglandular tissue, and 0.82 ($\pm$0.12) for tumors on T1-weighted images. The model generated accurate 3D reconstructions enabling visualization of complex anatomical features. Clinician interviews indicated improved planning, intraoperative navigation, and decision support. Integration of 3D visualization enhanced patient education, communication, and understanding. This human-in-the-loop machine learning approach successfully generalizes algorithms for 3D reconstruction and anatomical segmentation across patient datasets, offering enhanced visualization for clinicians, improved preoperative planning, and more effective patient education, facilitating shared decision-making and empowering informed patient choices across medical applications.

Structural magnetic resonance imaging (sMRI) combined with deep learning has achieved remarkable progress in the prediction and diagnosis of Alzheimer's disease (AD). Existing studies have used CNN and transformer to build a well-performing network, but most of them are based on pretraining or ignoring the asymmetrical character caused by brain disorders. We propose an end-to-end network for the detection of disease-based asymmetric induced by left and right brain atrophy which consist of 3D CNN Encoder and Symmetry Interactive Transformer (SIT). Following the inter-equal grid block fetch operation, the corresponding left and right hemisphere features are aligned and subsequently fed into the SIT for diagnostic analysis. SIT can help the model focus more on the regions of asymmetry caused by structural changes, thus improving diagnostic performance. We evaluated our method based on the ADNI dataset, and the results show that the method achieves better diagnostic accuracy (92.5\%) compared to several CNN methods and CNNs combined with a general transformer. The visualization results show that our network pays more attention in regions of brain atrophy, especially for the asymmetric pathological characteristics induced by AD, demonstrating the interpretability and effectiveness of the method.

Automatic quantification of intramyocardial motion and strain from tagging MRI remains an important but challenging task. We propose a method using implicit neural representations (INRs), conditioned on learned latent codes, to predict continuous left ventricular (LV) displacement -- without requiring inference-time optimisation. Evaluated on 452 UK Biobank test cases, our method achieved the best tracking accuracy (2.14 mm RMSE) and the lowest combined error in global circumferential (2.86%) and radial (6.42%) strain compared to three deep learning baselines. In addition, our method is $\sim$380$\times$ faster than the most accurate baseline. These results highlight the suitability of INR-based models for accurate and scalable analysis of myocardial strain in large CMR datasets.

High-intensity focused ultrasound (HIFU) is a non-invasive technique for treating uterine fibroids, and the accurate prediction of its therapeutic efficacy depends on precise quantification of the intratumoral heterogeneity. However, existing methods still have limitations in characterizing intratumoral heterogeneity, which restricts the accuracy of efficacy prediction. To this end, this study proposes a deep learning model with a parallel architecture of ResNet and ViT (Res-ViT) to verify whether the synergistic characterization of local texture and global spatial features can improve the accuracy of HIFU efficacy prediction. This study enrolled patients with uterine fibroids who underwent HIFU treatment from Center A (training set: N = 272; internal validation set: N = 92) and Center B (external test set: N = 125). Preoperative T2-weighted magnetic resonance images were used to develop the Res-ViT model for predicting immediate post-treatment non-perfused volume ratio (NPVR) ≥ 80%. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) and compared against independent Radiomics, ResNet-18, and ViT models. The Res-ViT model outperformed all standalone models across both internal (AUC = 0.895, 95% CI: 0.857-0.987) and external (AUC = 0.853, 95% CI: 0.776-0.921) test sets. SHAP analysis identified the ResNet branch as the predominant decision-making component (feature contribution: 55.4%). The visualization of Gradient-weighted Class Activation Mapping (Grad-CAM) shows that the key regions attended by Res-ViT have higher spatial overlap with the postoperative non-ablated fibroid tissue. The proposed Res-ViT model demonstrates that the fusion strategy of local and global features is an effective method for quantifying uterine fibroid heterogeneity, significantly enhancing the accuracy of HIFU efficacy prediction.

<i>"Just Accepted" papers have undergone full peer review and have been accepted for publication in <i>Radiology: Artificial Intelligence</i>. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content.</i> Purpose To assess the effectiveness of an explainable deep learning (DL) model, developed using multiparametric MRI (mpMRI) features, in improving diagnostic accuracy and efficiency of radiologists for classification of focal liver lesions (FLLs). Materials and Methods FLLs ≥ 1 cm in diameter at mpMRI were included in the study. nn-Unet and Liver Imaging Feature Transformer (LIFT) models were developed using retrospective data from one hospital (January 2018-August 2023). nnU-Net was used for lesion segmentation and LIFT for FLL classification. External testing was performed on data from three hospitals (January 2018-December 2023), with a prospective test set obtained from January 2024 to April 2024. Model performance was compared with radiologists and impact of model assistance on junior and senior radiologist performance was assessed. Evaluation metrics included the Dice similarity coefficient (DSC) and accuracy. Results A total of 2131 individuals with FLLs (mean age, 56 ± [SD] 12 years; 1476 female) were included in the training, internal test, external test, and prospective test sets. Average DSC values for liver and tumor segmentation across the three test sets were 0.98 and 0.96, respectively. Average accuracy for features and lesion classification across the three test sets were 93% and 97%, respectively. LIFT-assisted readings improved diagnostic accuracy (average 5.3% increase, <i>P</i> < .001), reduced reading time (average 34.5 seconds decrease, <i>P</i> < .001), and enhanced confidence (average 0.3-point increase, <i>P</i> < .001) of junior radiologists. Conclusion The proposed DL model accurately detected and classified FLLs, improving diagnostic accuracy and efficiency of junior radiologists. ©RSNA, 2025.