Introduction: Previous voxel‐based morphometry (VBM) studies have revealed changes in brain structure in patients with vestibular migraine (VM); these findings have improved the present understanding of pathophysiology. Few other studies have assessed the association between structural changes and the severity of dizziness in VM. This study aimed to examine the structural changes and cortical morphometric features associated with migraine and vertigo attacks in patients with VM.Methods: Twenty patients with VM and 20 healthy normal volunteers were scanned on a 3‐tesla MRI scanner. The gray matter volume (GMV) was estimated using the automated Computational Anatomy Toolbox (CAT12). The relationship between clinical parameters and morphometric abnormalities was also analyzed in VM.Results: Compared with controls, VM patients have decreased GMV in the prefrontal cortex (PFC), posterior insula–operculum regions, inferior parietal gyrus, and supramarginal gyrus. Moreover, patient scores on the Dizziness Handicap Inventory (DHI) score showed a negative correlation with GMV in the posterior insula–operculum regions.Conclusion: These findings demonstrated abnormality in the central vestibular cortex and correlations between dizziness severity and GMV in core regions of the vestibular cortex of VM patients, suggesting a pathophysiological role of these core vestibular regions in VM patients.

Objective: The aim of this study was to establish a predictive nomogram for predicting prostate cancer (PCa) in patients with gray-zone prostate-specific antigen (PSA) levels (4–10.0 ng/mL) based on radiomics and other traditional clinical parameters. Methods: In all, 274 patients with gray-zone PSA levels were included in this retrospective study. They were randomly divided into training and validation sets (n = 191 and 83, respectively). Data on the clinical risk factors related to PCa with gray-zone PSA levels (such as Prostate Imaging Reporting and Data System, version 2.1 [PI-RADS V2.1] category, age, prostate volume, and serum PSA level) were collected for all patients. Lesion volumes of interest (VOI) from T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) imaging were annotated by two radiologists. The radiomics model, clinical model, and combined prediction model, which was presented on a nomogram by incorporating the radiomics signature and clinical and radiological risk factors for PCa, were developed using logistic regression. The area under the receiver operator characteristic (AUC-ROC) and decision, calibration curve were used to compare the three models for the diagnosis of PCa with gray-zone PSA levels. Results: The predictive nomogram (AUC: 0.953) incorporating the radiomics score and PI-RADS V2.1 category, age, and the radiomics model (AUC: 0.941) afforded much higher diagnostic efficacy than the clinical model (AUC: 0.866). The addition of the rad score could improve the discriminatory performance of the clinical model. The decision curve analysis indicated that the radiomics or combined model could be more beneficial compared to the clinical model for the prediction of PCa. The nomogram showed good agreement for detecting PCa with gray-zone PSA levels between prediction and histopathologic confirmation. Conclusion: The nomogram, which combined the radiomics score and PI-RADS V2.1 category and age, is an effective and non-invasive method for predicting PCa. Furthermore, as well as good calibration and is clinically useful, which could reduce unnecessary prostate biopsies in patients having PCa with gray-zone PSA levels.

Objectives: This study aims to investigate the efficacy of a novel habitat-based histogram features derived from amide proton transfer-weighted (APTw) MRI for predicting BRAF mutation status in RC, compared and combined with diffusion-weighted imaging (DWI), and the potential for prognostic stratification. Methods: This study prospectively enrolled 269 patients with RC from June 2021 to August 2025, divided into a training set (n=188) and a testing set (n= 81) using a fixed random seed. According to BRAF status, patients were categorized into a wild-type group (n=214) and a mutant group (n=55). K-means clustering was applied to partition the tumors into 7 sub-regions, from which corresponding histogram features of APTw and apparent diffusion coefficient (ADC) maps were extracted. The extracted features were subsequently filtered using stepwise regression. The predictive performance of five models—namely, the habitat-based APTw model, habitat-based ADC model, habitat-based APTw+ADC combined model, clinical factors model, and the comprehensive model integrating all features—was evaluated using receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA). Additionally, Kaplan-Meier survival curves, constructed based on the combined nomogram, were used to assess 2-year disease-free survival (DFS) in the entire cohort. Results: The K-means model with K = 7 demonstrated optimal predictive power for BRAF mutation, outperforming models with K = 5 or 6, with the mutant group showing significant differences in APT/ADC histogram features in specific sub-regions compared to the wild-type group. The nomogram integrating APTw/ADC subregion histogram features and the clinical factors showed superior discrimination, achieving an AUC of 0.83 (95% CI: 0.707-0.952), which was significantly higher than that of the habitat-based APTw model (0.733), habitat-based ADC model (0.71), or clinical factors model (0.661) (all P < 0.05) in the testing set. Furthermore, patients stratified into the mutant group by the nomogram had a significantly worse 2-year disease-free survival (DFS) than those in the wild-type group ( P < 0.05). Conclusion: Habitat-based APT/ADC histogram features combined with clinical model showed good performance in predicting BRAF mutation and DFS of RC patients, which could provide valuable information for individualized treatment.