Surgical resection is an effective treatment for medically refractory mesial temporal lobe epilepsy (mTLE), however, more than one-third of patients fail to achieve seizure freedom after surgery. This study aimed to evaluate preoperative individual morphometric network characteristics and develop a machine learning model to predict surgical outcome in mTLE. This multicentre, retrospective study included 189 mTLE patients who underwent unilateral temporal lobectomy and 78 normal controls between February 2018 and June 2023. Postoperative seizure outcomes were categorized as seizure-free (SF, n = 125) or non-seizure-free (NSF, n = 64) at a minimum of one-year follow-up. The preoperative individualized structural covariance network (iSCN) derived from T1-weighted MRI was constructed for each patient by calculating deviations from the control-based reference distribution, and further divided into the surgery network and the surgically spared network using a standard resection mask by merging each patient's individual lacuna. Regional features were selected separately from bilateral, ipsilateral and contralateral iSCN abnormalities to train support vector machine models, validated in two independent external datasets. NSF patients showed greater iSCN deviations from the normative distribution in the surgically spared network compared to SF patients (P = 0.02). These deviations were widely distributed in the contralateral functional modules (P < 0.05, false discovery rate corrected). Seizure outcome was optimally predicted by the contralateral iSCN features, with an accuracy of 82% (P < 0.05, permutation test) and an area under the receiver operating characteristic curve (AUC) of 0.81, with the default mode and fronto-parietal areas contributing most. External validation in two independent cohorts showed accuracy of 80% and 88%, with AUC of 0.80 and 0.82, respectively, emphasizing the generalizability of the model. This study provides reliable personalized structural biomarkers for predicting surgical outcome in mTLE and has the potential to assist tailored surgical treatment strategies.

Alzheimer's disease (AD) constitutes a neurodegenerative disorder predominantly observed in the geriatric population. If AD can be diagnosed early, both in terms of prevention and treatment, it is very beneficial to patients. Therefore, our team proposed a novel deep learning model named 3D-CNN-VSwinFormer. The model consists of two components: the first part is a 3D CNN equipped with a 3D Convolutional Block Attention Module (3D CBAM) module, and the second part involves a fine-tuned Video Swin Transformer. Our investigation extracts features from subject-level 3D Magnetic resonance imaging (MRI) data, retaining only a single 3D MRI image per participant. This method circumvents data leakage and addresses the issue of 2D slices failing to capture global spatial information. We utilized the ADNI dataset to validate our proposed model. In differentiating between AD patients and cognitively normal (CN) individuals, we achieved accuracy and AUC values of 92.92% and 0.9660, respectively. Compared to other studies on AD and CN recognition, our model yielded superior results, enhancing the efficiency of AD diagnosis.

Sudden cardiac death from ventricular arrhythmias is a leading cause of mortality worldwide. Arrhythmic death prognostication is challenging in patients with hypertrophic cardiomyopathy (HCM), a setting where current clinical guidelines show low performance and inconsistent accuracy. Here, we present a deep learning approach, MAARS (Multimodal Artificial intelligence for ventricular Arrhythmia Risk Stratification), to forecast lethal arrhythmia events in patients with HCM by analyzing multimodal medical data. MAARS' transformer-based neural networks learn from electronic health records, echocardiogram and radiology reports, and contrast-enhanced cardiac magnetic resonance images, the latter being a unique feature of this model. MAARS achieves an area under the curve of 0.89 (95% confidence interval (CI) 0.79-0.94) and 0.81 (95% CI 0.69-0.93) in internal and external cohorts and outperforms current clinical guidelines by 0.27-0.35 (internal) and 0.22-0.30 (external). In contrast to clinical guidelines, it demonstrates fairness across demographic subgroups. We interpret MAARS' predictions on multiple levels to promote artificial intelligence transparency and derive risk factors warranting further investigation.

Accurate segmentation of white matter hyperintensities (WMH) is crucial for clinical decision-making, particularly in the context of multiple sclerosis. However, domain shifts, such as variations in MRI machine types or acquisition parameters, pose significant challenges to model calibration and uncertainty estimation. This comparative study investigates the impact of domain shift on WMH segmentation, proposing maximum-entropy regularization techniques to enhance model calibration and uncertainty estimation. The purpose is to identify errors appearing after model deployment in clinical scenarios using predictive uncertainty as a proxy measure, since it does not require ground-truth labels to be computed. We conducted experiments using a classic U-Net architecture and evaluated maximum entropy regularization schemes to improve model calibration under domain shift on two publicly available datasets: the WMH Segmentation Challenge and the 3D-MR-MS dataset. Performance is assessed with Dice coefficient, Hausdorff distance, expected calibration error, and entropy-based uncertainty estimates. Entropy-based uncertainty estimates can anticipate segmentation errors, both in-distribution and out-of-distribution, with maximum-entropy regularization further strengthening the correlation between uncertainty and segmentation performance, while also improving model calibration under domain shift. Maximum-entropy regularization improves uncertainty estimation for WMH segmentation under domain shift. By strengthening the relationship between predictive uncertainty and segmentation errors, these methods allow models to better flag unreliable predictions without requiring ground-truth annotations. Additionally, maximum-entropy regularization contributes to better model calibration, supporting more reliable and safer deployment of deep learning models in multi-center and heterogeneous clinical environments.

IntroductionThe urethra is a recommended avoidance structure for prostate cancer treatment. However, even subspecialist physicians often struggle to accurately identify the urethra on available imaging. Automated segmentation tools show promise, but a lack of reliable ground truth or appropriate evaluation standards has hindered validation and clinical adoption. This study aims to establish a reference-standard dataset with expert consensus contours, define clinically meaningful evaluation metrics, and assess the performance and generalizability of a deep-learning-based segmentation model. Materials and MethodsA multidisciplinary panel of four experienced subspecialists in prostate MRI generated consensus contours of the male urethra for 71 patients across six imaging centers. Four of those cases were previously used in an international study (PURE-MRI), wherein 62 physicians attempted to contour the prostate and urethra on the patient images. Separately, we developed a deep-learning AI model for urethra segmentation using another 151 cases from one center and evaluated it against the consensus reference standard and compared to human performance using Dice Score, percent urethra Coverage, and Maximum 2D (axial, in-plane) Hausdorff Distance (HD) from the reference standard. ResultsIn the PURE-MRI dataset, the AI model outperformed most physicians, achieving a median Dice of 0.41 (vs. 0.33 for physicians), Coverage of 81% (vs. 36%), and Max 2D HD of 1.8 mm (vs. 1.6 mm). In the larger dataset, performance remained consistent, with a Dice of 0.40, Coverage of 89%, and Max 2D HD of 2.0 mm, indicating strong generalizability across a broader patient population and more varied imaging conditions. ConclusionWe established a multidisciplinary consensus benchmark for segmentation of the urethra. The deep-learning model performs comparably to specialist physicians and demonstrates consistent results across multiple institutions. It shows promise as a clinical decision-support tool for accurate and reliable urethra segmentation in prostate cancer radiotherapy planning and studies of dose-toxicity associations.

Temporomandibular disorder (TMD) patients experience a variety of clinical symptoms, and MRI is the most effective tool for diagnosing temporomandibular joint (TMJ) disc displacement. This study aimed to develop a transformer-based deep learning model to generate T2-weighted (T2w) images from proton density-weighted (PDw) images, reducing MRI scan time for TMD patients. A dataset of 7226 images from 178 patients who underwent TMJ MRI examinations was used. The proposed model employed a generative adversarial network framework with a TransUNet architecture as the generator for image translation. Additionally, a disc segmentation decoder was integrated to improve image quality in the TMJ disc region. The model performance was evaluated using metrics such as the structural similarity index measure (SSIM), learned perceptual image patch similarity (LPIPS), and Fréchet inception distance (FID). Three experienced oral radiologists also performed a qualitative assessment through the mean opinion score (MOS). The model demonstrated high performance in generating T2w images from PDw images, achieving average SSIM, LPIPS, and FID values of 82.28%, 2.46, and 23.85, respectively, in the disc region. The model also obtained an average MOS score of 4.58, surpassing other models. Additionally, the model showed robust segmentation capabilities for the TMJ disc. The proposed model, integrating a transformer and a disc segmentation task, demonstrated strong performance in MR image generation, both quantitatively and qualitatively. This suggests its potential clinical significance in reducing MRI scan times for TMD patients while maintaining high image quality.

To generate virtual T1 contrast-enhanced (T1CE) sequences from plain spinal MRI sequences using the denoising diffusion probabilistic model (DDPM) and to compare its performance against one baseline model pix2pix and three advanced models. A total of 1195 consecutive spinal tumor patients who underwent contrast-enhanced MRI at two hospitals were divided into a training set (n = 809, 49 ± 17 years, 437 men), an internal test set (n = 203, 50 ± 16 years, 105 men), and an external test set (n = 183, 52 ± 16 years, 94 men). Input sequences were T1- and T2-weighted images, and T2 fat-saturation images. The output was T1CE images. In the test set, one radiologist read the virtual images and marked all visible enhancing lesions. Results were evaluated using sensitivity (SE) and false discovery rate (FDR). We compared differences in lesion size and enhancement degree between reference and virtual images, and calculated signal-to-noise (SNR) and contrast-to-noise ratios (CNR) for image quality assessment. In the external test set, the mean squared error was 0.0038±0.0065, and structural similarity index 0.78±0.10. Upon evaluation by the reader, the overall SE of the generated T1CE images was 94% with FDR 2%. There was no difference in lesion size or signal intensity ratio between the reference and generated images. The CNR was higher in the generated images than the reference images (9.241 vs. 4.021; P＜0.001). The proposed DDPM demonstrates potential as an alternative to gadolinium contrast in spinal MRI examinations of oncologic patients.

This study constructed an interpretable machine learning model based on multi-parameter MRI sub-region habitat radiomics and clinicopathological features, aiming to preoperatively evaluate the microsatellite instability (MSI) status of rectal cancer (RC) patients. This retrospective study recruited 291 rectal cancer patients with pathologically confirmed MSI status and randomly divided them into a training cohort and a testing cohort at a ratio of 8:2. First, the K-means method was used for cluster analysis of tumor voxels, and sub-region radiomics features and classical radiomics features were respectively extracted from multi-parameter MRI sequences. Then, the synthetic minority over-sampling technique method was used to balance the sample size, and finally, the features were screened. Prediction models were established using logistic regression based on clinicopathological variables, classical radiomics features, and MSI-related sub-region radiomics features, and the contribution of each feature to the model decision was quantified by the Shapley-Additive-Explanations (SHAP) algorithm. The area under the curve (AUC) of the sub-region radiomics model in the training and testing groups was 0.848 and 0.8, respectively, both better than that of the classical radiomics and clinical models. The combined model performed the best, with AUCs of 0.908 and 0.863 in the training and testing groups, respectively. We developed and validated a robust combined model that integrates clinical variables, classical radiomics features, and sub-region radiomics features to accurately determine the MSI status of RC patients. We visualized the prediction process using SHAP, enabling more effective personalized treatment plans and ultimately improving RC patient survival rates.

To investigate the effect of accelerated deep-learning (DL) multi-b-value DWI (Mb-DWI) on acquisition time, image quality, and predictive ability of microvascular invasion (MVI) in BCLC stage A hepatocellular carcinoma (HCC), compared to standard Mb-DWI. Patients who underwent liver MRI were prospectively collected. Subjective image quality, signal-to-noise ratio (SNR), lesion contrast-to-noise ratio (CNR), and Mb-DWI-derived parameters from various models (mono-exponential model, intravoxel incoherent motion, diffusion kurtosis imaging, and stretched exponential model) were calculated and compared between the two sequences. The Mb-DWI parameters of two sequences were compared between MVI-positive and MVI-negative groups, respectively. ROC and logistic regression analysis were performed to evaluate and identify the predictive performance. The study included 118 patients. 48/118 (40.67%) lesions were identified as MVI positive. DL Mb-DWI significantly reduced acquisition time by 52.86%. DL Mb-DWI produced significantly higher overall image quality, SNR, and CNR than standard Mb-DWI. All diffusion-related parameters except pseudo-diffusion coefficient showed significant differences between the two sequences. Both in DL and standard Mb-DWI, the apparent diffusion coefficient, true diffusion coefficient (D), perfusion fraction (f), mean diffusivity (MD), mean kurtosis (MK), and distributed diffusion coefficient (DDC) values were significantly different between MVI-positive and MVI-negative groups. The combination of D, f, and MK yield the highest AUC of 0.912 and 0.928 in standard and DL sequences, with no significant difference regarding the predictive efficiency. The DL Mb-DWI significantly reduces acquisition time and improves image quality, with comparable predictive performance to standard Mb-DWI in discriminating MVI status in BCLC stage A HCC.

To develop and validate a machine learning-based prediction model for the use of multiparametric magnetic resonance imaging(MRI) to predict benign and malignant lesions in the testis. The study retrospectively enrolled 148 patients with pathologically confirmed benign and malignant testicular lesions, dividing them into: training set (n=103) and validation set (n=45). Radiomics characteristics were derived from T2-weighted(T2WI)、contrast-enhanced T1-weighted(CE-T1WI)、diffusion-weighted imaging(DWI) and Apparent diffusion coefficient(ADC) MRI images, followed by feature selection. A machine learning-based combined model was developed by incorporating radiomics scores (rad scores) from the optimal radiomics model along with clinical predictors. Draw the receiver operating characteristic (ROC) curve and use the area under the curve (AUC) to evaluate and compare the predictive performance of each model. The diagnostic efficacy of the various machine learning models was evaluated using the Delong test. Radiomics features were extracted from four sequence-based groups(CE-T1WI+DWI+ADC+T2WI), and the model that combined Logistic Regression(LR) machine learning showed the best performance in the radiomics model. The clinical model identified one independent predictors. The combined clinical-radiomics model showed the best performance, whose AUC value was 0.932(95% confidence intervals(CI)0.868-0.978), sensitivity was 0.875, specificity was 0.871 and accuracy was 0.884 in validation set. The combined clinical-radiomics model can be used as a reliable tool to predict benign and malignant testicular lesions and provide a reference for clinical treatment method decisions.