Objective To develop an MRI habitat atlas that is robust across different haemorrhage stages for haemorrhagic brainstem cavernous malformations (BSCMs) and to integrate habitat-derived imaging biomarkers with clinical variables for precise prediction of symptomatic re-haemorrhage. Methods A retrospective cohort of 205 eligible patients from Huashan Main Campus (2015–2020) was randomly divided into a discovery set (n=147) for model development and an internal validation set (n=58). External validation was performed using a prospective temporal cohort from Huashan West Campus (2021–2023, n=94). Each lesion was segmented into six biologically distinct habitats via unsupervised clustering of multiparametric MRI. Habitat-derived radiomic features were combined with clinical variables into a multimodal logistic regression model, which was rigorously validated both internally and temporally. Results The 2-year cumulative re-haemorrhage rate was 81% in high-risk lesions versus 26% in low-risk lesions. The combined habitat–clinical model achieved an area under the curve (AUC) of 0.833 (95% CI 0.777 to 0.889) in the training set and 0.851 in the external validation set, significantly outperforming both imaging-only (∆AUC+0.07, p=0.008) and clinical-only models (∆AUC+0.17, p<0.001). The model demonstrated excellent calibration and provided net clinical benefit across decision thresholds of 5%–75%. Conclusion The multimodal MRI habitat atlas provides a robust and reproducible tool for individualised re-haemorrhage risk stratification in BSCMs, facilitating clinical decision-making for surveillance or intervention while reducing the likelihood of overtreatment.
Gui et al. (Wed,) studied this question.