<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 develop a transformer-based deep learning model-MR-Transformer-that leverages ImageNet pretraining and three-dimensional (3D) spatial correlations to predict the progression of knee osteoarthritis to TKR using MRI. Materials and Methods This retrospective study included 353 case-control matched pairs of coronal intermediate-weighted turbo spin-echo (COR-IW-TSE) and sagittal intermediate-weighted turbo spin-echo with fat suppression (SAG-IW-TSE-FS) knee MRIs from the Osteoarthritis Initiative (OAI) database, with a follow-up period up to 9 years, and 270 case-control matched pairs of coronal short-tau inversion recovery (COR-STIR) and sagittal proton density fat-saturated (SAG-PD-FAT-SAT) knee MRIs from the Multicenter Osteoarthritis Study (MOST) database, with a follow-up period up to 7 years. Performance of the MR-Transformer to predict the progression of knee osteoarthritis was compared with that of existing state-of-the-art deep learning models (TSE-Net, 3DMeT, and MRNet) using sevenfold nested cross-validation across the four MRI tissue sequences. Results MR-Transformer achieved areas under the receiver operating characteristic curves (AUCs) of 0.88 (95% CI: 0.85, 0.91), 0.88 (95% CI: 0.85, 0.90), 0.86 (95% CI: 0.82, 0.89), and 0.84 (95% CI: 0.81, 0.87) for COR-IW-TSE, SAG-IW-TSE-FS, COR-STIR, and SAG-PD-FAT-SAT, respectively. The model achieved a higher AUC than that of 3DMeT for all MRI sequences (<i>P</i> < .001). The model showed the highest sensitivity of 83% (95% CI: 78, 87%) and specificity of 83% (95% CI: 76, 88%) for the COR-IW-TSE MRI sequence. Conclusion Compared with the existing deep learning models, the MR-Transformer exhibited state-of-the-art performance in predicting the progression of knee osteoarthritis to TKR using MRIs. ©RSNA, 2025.

Bronchopulmonary dysplasia (BPD) is a chronic lung disease affecting 35% of extremely low birth weight infants. Defined by oxygen dependence at 36 weeks postmenstrual age, it causes lifelong respiratory complications. However, preventive interventions carry severe risks, including neurodevelopmental impairment, ventilator-induced lung injury, and systemic complications. Therefore, early BPD prognosis and prediction of BPD outcome is crucial to avoid unnecessary toxicity in low risk infants. Admission radiographs of extremely preterm infants are routinely acquired within 24h of life and could serve as a non-invasive prognostic tool. In this work, we developed and investigated a deep learning approach using chest X-rays from 163 extremely low-birth-weight infants ($\leq$32 weeks gestation, 401-999g) obtained within 24 hours of birth. We fine-tuned a ResNet-50 pretrained specifically on adult chest radiographs, employing progressive layer freezing with discriminative learning rates to prevent overfitting and evaluated a CutMix augmentation and linear probing. For moderate/severe BPD outcome prediction, our best performing model with progressive freezing, linear probing and CutMix achieved an AUROC of 0.78 $\pm$ 0.10, balanced accuracy of 0.69 $\pm$ 0.10, and an F1-score of 0.67 $\pm$ 0.11. In-domain pre-training significantly outperformed ImageNet initialization (p = 0.031) which confirms domain-specific pretraining to be important for BPD outcome prediction. Routine IRDS grades showed limited prognostic value (AUROC 0.57 $\pm$ 0.11), confirming the need of learned markers. Our approach demonstrates that domain-specific pretraining enables accurate BPD prediction from routine day-1 radiographs. Through progressive freezing and linear probing, the method remains computationally feasible for site-level implementation and future federated learning deployments.

Cortical lesions (CLs) have emerged as valuable biomarkers in multiple sclerosis (MS), offering high diagnostic specificity and prognostic relevance. However, their routine clinical integration remains limited due to subtle magnetic resonance imaging (MRI) appearance, challenges in expert annotation, and a lack of standardized automated methods. We propose a comprehensive multi-centric benchmark of CL detection and segmentation in MRI. A total of 656 MRI scans, including clinical trial and research data from four institutions, were acquired at 3T and 7T using MP2RAGE and MPRAGE sequences with expert-consensus annotations. We rely on the self-configuring nnU-Net framework, designed for medical imaging segmentation, and propose adaptations tailored to the improved CL detection. We evaluated model generalization through out-of-distribution testing, demonstrating strong lesion detection capabilities with an F1-score of 0.64 and 0.5 in and out of the domain, respectively. We also analyze internal model features and model errors for a better understanding of AI decision-making. Our study examines how data variability, lesion ambiguity, and protocol differences impact model performance, offering future recommendations to address these barriers to clinical adoption. To reinforce the reproducibility, the implementation and models will be publicly accessible and ready to use at https://github.com/Medical-Image-Analysis-Laboratory/ and https://doi.org/10.5281/zenodo.15911797.

Radiologists rely on eye movements to navigate and interpret medical images. A trained radiologist possesses knowledge about the potential diseases that may be present in the images and, when searching, follows a mental checklist to locate them using their gaze. This is a key observation, yet existing models fail to capture the underlying intent behind each fixation. In this paper, we introduce a deep learning-based approach, RadGazeIntent, designed to model this behavior: having an intention to find something and actively searching for it. Our transformer-based architecture processes both the temporal and spatial dimensions of gaze data, transforming fine-grained fixation features into coarse, meaningful representations of diagnostic intent to interpret radiologists' goals. To capture the nuances of radiologists' varied intention-driven behaviors, we process existing medical eye-tracking datasets to create three intention-labeled subsets: RadSeq (Systematic Sequential Search), RadExplore (Uncertainty-driven Exploration), and RadHybrid (Hybrid Pattern). Experimental results demonstrate RadGazeIntent's ability to predict which findings radiologists are examining at specific moments, outperforming baseline methods across all intention-labeled datasets.

Deep learning has significantly advanced the field of medical image classification, particularly with the adoption of Convolutional Neural Networks (CNNs). Various deep learning frameworks such as Keras, PyTorch and JAX offer unique advantages in model development and deployment. However, their comparative performance in medical imaging tasks remains underexplored. This study presents a comprehensive analysis of CNN implementations across these frameworks, using the PathMNIST dataset as a benchmark. We evaluate training efficiency, classification accuracy and inference speed to assess their suitability for real-world applications. Our findings highlight the trade-offs between computational speed and model accuracy, offering valuable insights for researchers and practitioners in medical image analysis.

Alzheimer's disease (AD) frequently co-occurs with depressive symptoms, exacerbating both cognitive decline and clinical complexity, yet the neural substrates linking this co-occurrence remain poorly understood. We aimed to investigate the role of basal forebrain-limbic system circuit dysregulation in the interaction between cognitive impairment and depressive symptoms, identifying potential biomarkers for early diagnosis and intervention. This cross-sectional study included participants stratified into normal controls (NC), cognitive impairment without depression (CI-nD), and cognitive impairment with depression (CI-D). Multimodal MRI (structural, diffusion, functional, perfusion, iron-sensitive imaging) and plasma biomarkers were analyzed. Machine learning models classified subgroups using neuroimaging features. CI-D exhibited distinct basal forebrain-limbic circuit alterations versus CI-nD and NC: (1) Elevated free-water fraction (FW) in basal forebrain subregions (Ch123/Ch4, p < 0.04), indicating early neuroinflammation; (2) Increased iron deposition in the anterior cingulate cortex and entorhinal cortex (p < 0.05); (3) Hyperperfusion and functional hyperactivity in Ch123 and amygdala; (4) Plasma neurofilamentlightchain exhibited correlated with hippocampal inflammation in CI-nD (p = 0.03) but linked to basal forebrain dysfunction in CI-D (p < 0.05). Multimodal support vector machine achieved 85 % accuracy (AUC=0.96) in distinguishing CI-D from CI-nD, with Ch123 and Ch4 as key discriminators. Pathway analysis in the CI-D group further revealed that FW-related neuroinflammation in the basal forebrain (Ch123/Ch4) indirectly contributed to cognitive impairment via structural atrophy. We identified a neuroinflammatory-cholinergic pathway in the basal forebrain as an early mechanism driving depression-associated cognitive decline. Multimodal imaging revealed distinct spatiotemporal patterns of circuit dysregulation, suggesting neuroinflammation and iron deposition precede structural degeneration. These findings position the basal forebrain-limbic system circuit as a therapeutic target and provide actionable biomarkers for early intervention in AD with depressive symptoms.

Quantifiable image patterns associated with disease progression and treatment response are critical tools for guiding individual treatment, and for developing novel therapies. Here, we show that unsupervised machine learning can identify a pattern vocabulary of liver tissue in magnetic resonance images that quantifies treatment response in diffuse liver disease. Deep clustering networks simultaneously encode and cluster patches of medical images into a low-dimensional latent space to establish a tissue vocabulary. The resulting tissue types capture differential tissue change and its location in the liver associated with treatment response. We demonstrate the utility of the vocabulary on a randomized controlled trial cohort of non-alcoholic steatohepatitis patients. First, we use the vocabulary to compare longitudinal liver change in a placebo and a treatment cohort. Results show that the method identifies specific liver tissue change pathways associated with treatment, and enables a better separation between treatment groups than established non-imaging measures. Moreover, we show that the vocabulary can predict biopsy derived features from non-invasive imaging data. We validate the method on a separate replication cohort to demonstrate the applicability of the proposed method.

Foundation models have significantly revolutionized the field of chest X-ray diagnosis with their ability to transfer across various diseases and tasks. However, previous works have predominantly utilized self-supervised learning from medical image-text pairs, which falls short in dense medical prediction tasks due to their sole reliance on such coarse pair supervision, thereby limiting their applicability to detailed diagnostics. In this paper, we introduce a Dense Chest X-ray Foundation Model (DCXFM), which utilizes mixed supervision types (i.e., text, label, and segmentation masks) to significantly enhance the scalability of foundation models across various medical tasks. Our model involves two training stages: we first employ a novel self-distilled multimodal pretraining paradigm to exploit text and label supervision, along with local-to-global self-distillation and soft cross-modal contrastive alignment strategies to enhance localization capabilities. Subsequently, we introduce an efficient cost aggregation module, comprising spatial and class aggregation mechanisms, to further advance dense prediction tasks with densely annotated datasets. Comprehensive evaluations on three tasks (phrase grounding, zero-shot semantic segmentation, and zero-shot classification) demonstrate DCXFM's superior performance over other state-of-the-art medical image-text pretraining models. Remarkably, DCXFM exhibits powerful zero-shot capabilities across various datasets in phrase grounding and zero-shot semantic segmentation, underscoring its superior generalization in dense prediction tasks.

Bronchopulmonary dysplasia (BPD) is a chronic lung disease affecting 35% of extremely low birth weight infants. Defined by oxygen dependence at 36 weeks postmenstrual age, it causes lifelong respiratory complications. However, preventive interventions carry severe risks, including neurodevelopmental impairment, ventilator-induced lung injury, and systemic complications. Therefore, early BPD prognosis and prediction of BPD outcome is crucial to avoid unnecessary toxicity in low risk infants. Admission radiographs of extremely preterm infants are routinely acquired within 24h of life and could serve as a non-invasive prognostic tool. In this work, we developed and investigated a deep learning approach using chest X-rays from 163 extremely low-birth-weight infants ($\leq$32 weeks gestation, 401-999g) obtained within 24 hours of birth. We fine-tuned a ResNet-50 pretrained specifically on adult chest radiographs, employing progressive layer freezing with discriminative learning rates to prevent overfitting and evaluated a CutMix augmentation and linear probing. For moderate/severe BPD outcome prediction, our best performing model with progressive freezing, linear probing and CutMix achieved an AUROC of 0.78 $\pm$ 0.10, balanced accuracy of 0.69 $\pm$ 0.10, and an F1-score of 0.67 $\pm$ 0.11. In-domain pre-training significantly outperformed ImageNet initialization (p = 0.031) which confirms domain-specific pretraining to be important for BPD outcome prediction. Routine IRDS grades showed limited prognostic value (AUROC 0.57 $\pm$ 0.11), confirming the need of learned markers. Our approach demonstrates that domain-specific pretraining enables accurate BPD prediction from routine day-1 radiographs. Through progressive freezing and linear probing, the method remains computationally feasible for site-level implementation and future federated learning deployments.

This study evaluates the predictive ability of multiparametric dual-energy computed tomography (multi-DECT) radiomics for tumor budding (TB) grade and prognosis in patients with colorectal cancer (CRC). This study comprised 510 CRC patients at two institutions. The radiomics features of multi-DECT images (including polyenergetic, virtual monoenergetic, iodine concentration [IC], and effective atomic number images) were screened to build radiomics models utilizing nine machine learning (ML) algorithms. An ML-based fusion model comprising clinical-radiological variables and radiomics features was developed. The assessment of model performance was conducted through the area under the receiver operating characteristic curve (AUC), while the model's interpretability was assessed by shapley additive explanation (SHAP). The prognostic significance of the fusion model was determined via survival analysis. The CT-reported lymph node status and normalized IC were used to develop a clinical-radiological model. Among the nine examined ML algorithms, the extreme gradient boosting (XGB) algorithm performed best. The XGB-based fusion model containing multi-DECT radiomics features outperformed the clinical-radiological model in predicting TB grade, demonstrating superior AUCs of 0.969 in the training cohort, 0.934 in the internal validation cohort, and 0.897 in the external validation cohort. The SHAP analysis identified variables influencing model predictions. Patients with a model-predicted high TB grade had worse recurrence-free survival (RFS) in both the training (P < 0.001) and internal validation (P = 0.016) cohorts. The XGB-based fusion model using multi-DECT radiomics could serve as a non-invasive tool to predict TB grade and RFS in patients with CRC preoperatively.