Clinical neurology and neurosurgery · 2026
Combinations of clinical factors and radiomics for the hemorrhage-free survival of brainstem cavernous malformation patients.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Huo Xulei, Yao Bohan, Guo Jia, Ma Sihan, Wang Hongwei, Weng Jiancong, Wang Ke, Wu Zhen, Li Da
Аннотация
OBJECTIVES: This study aimed to develop a MRI-based prognostic nomogram including radiomics and clinical information for brainstem cavernous malformation patients (BSCMs).
METHODS: 114 BSCMs were randomly divided into a training cohort and a validation cohort. Clinical and radiomics nomograms were constructed. Radiomics features were selected using three algorithms: univariate analysis, Pearson correlation, and the elastic net algorithm. A Cox regression model was employed to build the radiomics nomogram. The concordance index (C-index), time-independent receiver operating characteristic (ROC) analysis, and decision curve analysis (DCA) were used to evaluate the clinical utility of the nomogram.
RESULTS: The radiomics signature score was calculated using 11 radiomics features related to hemorrhage-free survival (HFS) from the training cohort. Patients were stratified into high-risk and low-risk groups based on the radiomics signature, with the low-risk group demonstrating significantly better HFS. Additionally, three clinical factors-number of hemorrhages, lesion size, and modified Rankin Scale score-along with the radiomics score were used to develop the radiomics nomogram. Calibration plots indicated good agreement between predicted and actual survival probabilities. The C-index for the training and validation cohorts was 0.784 and 0.787, respectively, in predicting HFS. The area under the curve was 72.51 and 76.41 for 3-year survival, and 67.62 and 72.57 for 5-year survival in the training and validation cohorts, respectively. The DCA curve demonstrated that the radiomics nomogram had superior clinical utility compared to the clinical model.
CONCLUSIONS: The radiomics nomogram showed great potential as a sensitive prognostic tool in predicting hemorrhage-free survival in BSCMs.
METHODS: 114 BSCMs were randomly divided into a training cohort and a validation cohort. Clinical and radiomics nomograms were constructed. Radiomics features were selected using three algorithms: univariate analysis, Pearson correlation, and the elastic net algorithm. A Cox regression model was employed to build the radiomics nomogram. The concordance index (C-index), time-independent receiver operating characteristic (ROC) analysis, and decision curve analysis (DCA) were used to evaluate the clinical utility of the nomogram.
RESULTS: The radiomics signature score was calculated using 11 radiomics features related to hemorrhage-free survival (HFS) from the training cohort. Patients were stratified into high-risk and low-risk groups based on the radiomics signature, with the low-risk group demonstrating significantly better HFS. Additionally, three clinical factors-number of hemorrhages, lesion size, and modified Rankin Scale score-along with the radiomics score were used to develop the radiomics nomogram. Calibration plots indicated good agreement between predicted and actual survival probabilities. The C-index for the training and validation cohorts was 0.784 and 0.787, respectively, in predicting HFS. The area under the curve was 72.51 and 76.41 for 3-year survival, and 67.62 and 72.57 for 5-year survival in the training and validation cohorts, respectively. The DCA curve demonstrated that the radiomics nomogram had superior clinical utility compared to the clinical model.
CONCLUSIONS: The radiomics nomogram showed great potential as a sensitive prognostic tool in predicting hemorrhage-free survival in BSCMs.