Ambiguity in medical image segmentation calls for models that capture full conditional distributions rather than a single point estimate. We present Prior-Guided Residual Diffusion (PGRD), a diffusion-based framework that learns voxel-wise distributions while maintaining strong calibration and practical sampling efficiency. PGRD embeds discrete labels as one-hot targets in a continuous space to align segmentation with diffusion modeling. A coarse prior predictor provides step-wise guidance; the diffusion network then learns the residual to the prior, accelerating convergence and improving calibration. A deep diffusion supervision scheme further stabilizes training by supervising intermediate time steps. Evaluated on representative MRI and CT datasets, PGRD achieves higher Dice scores and lower NLL/ECE values than Bayesian, ensemble, Probabilistic U-Net, and vanilla diffusion baselines, while requiring fewer sampling steps to reach strong performance.

Ultrasound (US) imaging is a critical tool in medical diagnostics, offering real-time visualization of physiological processes. One of its major advantages is its ability to capture temporal dynamics, which is essential for assessing motion patterns in applications such as cardiac monitoring, fetal development, and vascular imaging. Despite its importance, current deep learning models often overlook the temporal continuity of ultrasound sequences, analyzing frames independently and missing key temporal dependencies. To address this gap, we propose a method for learning effective temporal representations from ultrasound videos, with a focus on echocardiography-based ejection fraction (EF) estimation. EF prediction serves as an ideal case study to demonstrate the necessity of temporal learning, as it requires capturing the rhythmic contraction and relaxation of the heart. Our approach leverages temporally consistent masking and contrastive learning to enforce temporal coherence across video frames, enhancing the model's ability to represent motion patterns. Evaluated on the EchoNet-Dynamic dataset, our method achieves a substantial improvement in EF prediction accuracy, highlighting the importance of temporally-aware representation learning for real-time ultrasound analysis.

To evaluate a deep-learning algorithm for automated coronary artery analysis on ultrahigh-resolution photon-counting detector coronary computed tomography (CT) angiography and compared its performance to expert readers using invasive coronary angiography as reference. Thirty-two patients (mean age 68.6 years; 81 ​% male) underwent both energy-integrating detector and ultrahigh-resolution photon-counting detector CT within 30 days. Expert readers scored each image using the Coronary Artery Disease-Reporting and Data System classification, and compared to invasive angiography. After a three-month wash-out, one reader reanalyzed the photon-counting detector CT images assisted by the algorithm. Sensitivity, specificity, accuracy, inter-reader agreement, and reading times were recorded for each method. On 401 arterial segments, inter-reader agreement improved from substantial (κ ​= ​0.75) on energy-integrating detector CT to near-perfect (κ ​= ​0.86) on photon-counting detector CT. The algorithm alone achieved 85 ​% sensitivity, 91 ​% specificity, and 90 ​% accuracy on energy-integrating detector CT, and 85 ​%, 96 ​%, and 95 ​% on photon-counting detector CT. Compared to invasive angiography on photon-counting detector CT, manual and automated reads had similar sensitivity (67 ​%), but manual assessment slightly outperformed regarding specificity (85 ​% vs. 79 ​%) and accuracy (84 ​% vs. 78 ​%). When the reader was assisted by the algorithm, specificity rose to 97 ​% (p ​< ​0.001), accuracy to 95 ​%, and reading time decreased by 54 ​% (p ​< ​0.001). This deep-learning algorithm demonstrates high agreement with experts and improved diagnostic performance on photon-counting detector CT. Expert review augmented by the algorithm further increases specificity and dramatically reduces interpretation time.

Proliferative hepatocellular carcinoma (HCC) is an aggressive tumor with varying prognosis depending on the different disease stages and subsequent treatment. This study aims to develop and validate a deep learning radiomics (DLR) model based on contrast-enhanced CT to predict proliferative HCC and to implement risk prediction in patients treated with transarterial chemoembolization (TACE) and radiofrequency ablation (RFA). 312 patients (mean age, 58 years ± 10 [SD]; 261 men and 51 women) with HCC undergoing surgery at two medical centers were included, who were divided into a training set (<i>n</i> = 182), an internal test set (<i>n</i> = 46) and an external test set (<i>n</i> = 84). DLR features were extracted from preoperative contrast-enhanced CT images. Multiple machine learning algorithms were used to develop and validate proliferative HCC prediction models in training and test sets. Subsequently, patients from two independent new sets (RFA and TACE sets) were divided into high- and low-risk groups using the DLR score generated by the optimal model. The risk prediction value of DLR scores in recurrence-free survival (RFS) and time to progression (TTP) was examined separately in RFA and TACE sets. The DLR proliferative HCC prediction model demonstrated excellent predictive performance with an AUC of 0.906 (95% CI 0.861–0.952) in the training set, 0.901 (95% CI 0.779–1.000) in the internal test set and 0.837 (95% CI 0.746–0.928) in the external test set. The DLR score effectively enables risk prediction for patients in RFA and TACE sets. For the RFA set, the low-risk group had significantly longer RFS compared to the high-risk group (<i>P</i> = 0.037). Similarly, the low-risk group showed a longer TTP than the high-risk group for the TACE set (<i>P</i> = 0.034). The DLR-based contrast-enhanced CT model enables non-invasive prediction of proliferative HCC. Furthermore, the DLR risk prediction helps identify high-risk patients undergoing RFA or TACE, providing prognostic insights for personalized management. The online version contains supplementary material available at 10.1186/s12880-025-01913-9.

Machine learning based on clinical characteristics has the potential to predict coronary CT angiography (CCTA) findings and help guide resource utilisation. From the SCOT-HEART (Scottish Computed Tomography of the HEART) trial, data from 1769 patients was used to train and to test machine learning models (XGBoost, 10-fold cross validation, grid search hyperparameter selection). Two models were separately generated to predict the presence of coronary artery disease (CAD) and an increased burden of low-attenuation coronary artery plaque (LAP) using symptoms, demographic and clinical characteristics, electrocardiography and exercise tolerance testing (ETT). Machine learning predicted the presence of CAD on CCTA (area under the curve (AUC) 0.80, 95% CI 0.74 to 0.85) better than the 10-year cardiovascular risk score alone (AUC 0.75, 95% CI 0.70, 0.81, p=0.004). The most important features in this model were the 10-year cardiovascular risk score, age, sex, total cholesterol and an abnormal ETT. In contrast, the second model used to predict an increased LAP burden performed similarly to the 10-year cardiovascular risk score (AUC 0.75, 95% CI 0.70 to 0.80 vs AUC 0.72, 95% CI 0.66 to 0.77, p=0.08) with the most important features being the 10-year cardiovascular risk score, age, body mass index and total and high-density lipoprotein cholesterol concentrations. Machine learning models can improve prediction of the presence of CAD on CCTA, over the standard cardiovascular risk score. However, it was not possible to improve the prediction of an increased LAP burden based on clinical factors alone.

The respiratory system depends on complex biomechanical processes to enable gas exchange. The mechanical properties of the lung parenchyma, airways, vasculature, and surrounding structures play an essential role in overall ventilation efficacy. These complex biomechanical processes however are significantly altered in chronic obstructive pulmonary disease (COPD) due to emphysematous destruction of lung parenchyma, chronic airway inflammation, and small airway obstruction. Recent advancements computed tomography (CT) and magnetic resonance imaging (MRI) acquisition techniques, combined with sophisticated image post-processing algorithms and deep neural network integration, have enabled comprehensive quantitative assessment of lung structure, tissue deformation, and lung function at the tissue level. These methods have led to better phenotyping, therapeutic strategies and refined our understanding of pathological processes that compromise pulmonary function in COPD. In this review, we discuss recent developments in imaging and image processing methods for studying pulmonary biomechanics with specific focus on clinical applications for chronic obstructive pulmonary disease (COPD) including the assessment of regional ventilation, planning of endobronchial valve treatment, prediction of disease onset and progression, sizing of lungs for transplantation, and guiding mechanical ventilation. These advanced image-based biomechanical measurements when combined with clinical expertise play a critical role in disease management and personalized therapeutic interventions for patients with COPD.

Microscopic tumor cell infiltration beyond contrast-enhancing regions influences glioblastoma prognosis but remains undetectable using conventional MRI. To develop and evaluate the glioblastoma infiltrating area interactive detection framework (GIAIDF), an interactive deep-learning framework that integrates diffusion tensor imaging (DTI) biomarkers for identifying microscopic infiltration within peritumoral edema. Retrospective. A total of 73 training patients (51.13 ± 13.87 years; 47 M/26F) and 25 internal validation patients (52.82 ± 10.76 years; 14 M/11F) from Center 1; 25 external validation patients (47.29 ± 11.39 years; 16 M/9F) from Center 2; 13 prospective biopsy patients (45.62 ± 9.28 years; 8 M/5F) from Center 1. 3.0 T MRI including three-dimensional contrast-enhanced T1-weighted BRAVO sequence (repetition time = 7.8 ms, echo time = 3.0 ms, inversion time = 450 ms, slice thickness = 1 mm), three-dimensional T2-weighted fluid-attenuated inversion recovery (repetition time = 7000 ms, echo time = 120 ms, inversion time = 2000 ms, slice thickness = 1 mm), and diffusion tensor imaging (repetition time = 8500 ms, echo time = 63 ms, slice thickness = 2 mm). Histopathology of 25 stereotactic biopsy specimens served as the reference standard. Primary metrics included AUC, accuracy, sensitivity, and specificity. GIAIDF heatmaps were co-registered to biopsy trajectories using Ratio-FAcpcic (0.16-0.22) as interactive priors. ROC analysis (DeLong's method) for AUC; recall, precision, and F1 score for prediction validation. GIAIDF demonstrated recall = 0.800 ± 0.060, precision = 0.915 ± 0.057, F1 = 0.852 ± 0.044 in internal validation (n = 25) and recall = 0.778 ± 0.053, precision = 0.890 ± 0.051, F1 = 0.829 ± 0.040 in external validation (n = 25). Among 13 patients undergoing stereotactic biopsy, 25 peri-ED specimens were analyzed: 18 without tumor cell infiltration and seven with infiltration, achieving AUC = 0.929 (95% CI: 0.804-1.000), sensitivity = 0.714, specificity = 0.944, and accuracy = 0.880. Infiltrated sites showed significantly higher risk scores (0.549 ± 0.194 vs. 0.205 ± 0.175 in non-infiltrated sites, p < 0.001). This study has provided a potential tool, GIAIDF, to identify regions of GBM infiltration within areas of peri-ED based on preoperative MR images.

Assessing lymph node metastasis (LNM) involvement in patients with rectal cancer (RC) is fundamental in disease management. In this study, we used artificial intelligence (AI) technology to develop a segmentation model that automatically segments the tumor core area and mesangial tissue from magnetic resonance T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) images collected from 122 RC patients to improve the accuracy of LNM prediction, after which omics machine modeling was performed on the segmented ROI. An automatic segmentation model was developed using nn-UNet. This pipeline integrates deep learning (DL), specifically 3D U-Net, for semantic segmentation and image processing techniques such as resampling, normalization, connected component analysis, image registration, and radiomics features coupled with machine learning. The results showed that the DL segmentation method could effectively segment the tumor and mesangial areas from MR sequences (the median dice coefficient: 0.90 ± 0.08; mesorectum segmentation: 0.85 ± 0.36), and the radiological characteristics of rectal and mesangial tissues in T2WI and ADC images could help distinguish RC treatments. The nn-UNet model demonstrated promising preliminary results, achieving the highest area under the curve (AUC) values in various scenarios. In the evaluation encompassing both tumor lesions and mesorectum involvement, the model exhibited an AUC of 0.743, highlighting its strong discriminatory ability to predict a combined outcome involving both elements. Specifically targeting tumor lesions, the model achieved an AUC of 0.731, emphasizing its effectiveness in distinguishing between positive and negative cases of tumor lesions. In assessing the prediction of mesorectum involvement, the model displayed moderate predictive utility with an AUC of 0.753. The nn-UNet model demonstrated impressive performance across all evaluated scenarios, including combined tumor lesions and mesorectum involvement, tumor lesions alone, and mesorectum involvement alone. The online version contains supplementary material available at 10.1186/s12880-025-01878-9.

White matter hyperintensities (WMH) are commonly assessed using the Fazekas scale, a subjective visual grading system. Despite the emergence of deep learning models for automatic WMH grading, their application in stroke patients remains limited. This study aimed to develop and validate an automatic segmentation and grading model for WMH in stroke patients, utilizing spatial-probabilistic methods. We developed a two-step deep learning pipeline to predict Fazekas scale scores from T2-weighted FLAIR images. First, WMH segmentation was performed using a residual neural network based on the U-Net architecture. Then, Fazekas scale grading was carried out using a 3D convolutional neural network trained on the segmented WMH probability volumes. A total of 471 stroke patients from three different sources were included in the analysis. The performance metrics included area under the precision-recall curve (AUPRC), Dice similarity coefficient, and absolute error for WMH volume prediction. In addition, agreement analysis and quadratic weighted kappa were calculated to assess the accuracy of the Fazekas scale predictions. The WMH segmentation model achieved an AUPRC of 0.81 (95% CI, 0.55-0.95) and a Dice similarity coefficient of 0.73 (95% CI, 0.49-0.87) in the internal test set. The mean absolute error between the true and predicted WMH volumes was 3.1 ml (95% CI, 0.0 ml-15.9 ml), with no significant variation across Fazekas scale categories. The agreement analysis demonstrated strong concordance, with an R-squared value of 0.91, a concordance correlation coefficient of 0.96, and a systematic difference of 0.33 ml in the internal test set, and 0.94, 0.97, and 0.40 ml, respectively, in the external validation set. In predicting Fazekas scores, the 3D convolutional neural network achieved quadratic weighted kappa values of 0.951 for regression tasks and 0.956 for classification tasks in the internal test set, and 0.898 and 0.956, respectively, in the external validation set. The proposed deep learning pipeline demonstrated robust performance in automatic WMH segmentation and Fazekas scale grading from FLAIR images in stroke patients. This approach offers a reliable and efficient tool for evaluating WMH burden, which may assist in predicting future vascular events.

Glioblastoma multiforme (GBM) is a lethal brain tumor with limited therapies. NUF2, a kinetochore protein involved in cell cycle regulation, shows oncogenic potential in various cancers; however, its role in GBM pathogenesis remains unclear. In this study, we investigated NUF2's function and mechanisms in GBM and developed an MRI-based machine learning model to predict its expression non-invasively, and evaluated its potential as a therapeutic target and prognostic biomarker. Functional assays (proliferation, colony formation, migration, and invasion) and cell cycle analysis were conducted using NUF2-knockdown U87/U251 cells. Western blotting was performed to assess the expression levels of β-catenin and MMP-9. Bioinformatic analyses included pathway enrichment, immune infiltration, and single-cell subtype characterization. Using preoperative T1CE Magnetic Resonance Imaging sequences from 61 patients, we extracted 1037 radiomic features and developed a predictive model using Least Absolute Shrinkage and Selection Operator regression for feature selection and random forest algorithms for classification with rigorous cross-validation. NUF2 overexpression in GBM tissues and cells was correlated with poor survival (p < 0.01). Knockdown of NUF2 significantly suppressed malignant phenotypes (p < 0.05), induced G0/G1 arrest (p < 0.01), and increased sensitivity to TMZ treatment via the β-catenin/MMP9 pathway. The radiomic model achieved superior NUF2 prediction (AUC = 0.897) using six optimized features. Key features demonstrated associations with MGMT methylation and 1p/19q co-deletion, serving as independent prognostic markers. NUF2 drives GBM progression through β-catenin/MMP9 activation, establishing its dual role as a therapeutic target and a prognostic biomarker. The developed radiogenomic model enables precise non-invasive NUF2 evaluation, thereby advancing personalized GBM management. This study highlights the translational value of integrating molecular biology with artificial intelligence in neuro-oncology.