ArticleFrontiers in oncology2026
Habitat-based amide proton transfer-weighted MRI model for predicting BRAF mutation and prognostic stratification in rectal cancer.
Article in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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 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.
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