A multiregional multimodal machine learning model for predicting outcome of surgery for symptomatic hemorrhagic brainstem cavernous malformations.
Authors
Affiliations (10)
Affiliations (10)
- 1Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai.
- 5Shanghai Clinical Medical Center of Neurosurgery, Shanghai.
- 6School of Information Science and Technology, Fudan University, Shanghai.
- 7Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention (MICCAI) of Shanghai.
- 8Department of Radiology, Huashan Hospital, Fudan University, Shanghai.
- 9Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai.
- 10Huashan Institute of Medicine, Huashan Hospital, Shanghai.
- 11School of Data Science, Fudan University, Shanghai.
- 12Department of Neurosurgery, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu; and.
- 13Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
Abstract
Given that resection of brainstem cavernous malformations (BSCMs) ends hemorrhaging but carries a high risk of neurological deficits, it is necessary to develop and validate a model predicting surgical outcomes. This study aimed to construct a BSCM surgery outcome prediction model based on clinical characteristics and T2-weighted MRI-based radiomics. Two separate cohorts of patients undergoing BSCM resection were included as discovery and validation sets. Patient characteristics and imaging data were analyzed. An unfavorable outcome was defined as a modified Rankin Scale score > 2 at the 12-month follow-up. Image features were extracted from regions of interest within lesions and adjacent brainstem. A nomogram was constructed using the risk score from the optimal model. The discovery and validation sets comprised 218 and 49 patients, respectively (mean age 40 ± 14 years, 127 females); 63 patients in the discovery set and 35 in the validation set had an unfavorable outcome. The eXtreme Gradient Boosting imaging model with selected radiomics features achieved the best performance (area under the receiver operating characteristic curve [AUC] 0.82). Patients were stratified into high- and low-risk groups based on risk scores computed from this model (optimal cutoff 0.37). The final integrative multimodal prognostic model attained an AUC of 0.90, surpassing both the imaging and clinical models alone. Inclusion of BSCM and brainstem subregion imaging data in machine learning models yielded significant predictive capability for unfavorable postoperative outcomes. The integration of specific clinical features enhanced prediction accuracy.