ArticleNan fang yi ke da xue xue bao = Journal of Southern Medical University2024
[A predictive model for survival outcomes of glioma patients based on multi-parametric, multi-regional MRI radiomics features and clinical features].
Article in Nan fang yi ke da xue xue bao = Journal of Southern Medical University, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Predicting Recurrence Risk of Glioblastoma Based on Preoperative-Postoperative Longitudinal MRI: A Multicenter Study.Bioengineering (Basel, Switzerland) · 2026Article
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4 authors.
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Abstract
objectiveTo establish a predictive model for survival outcomes of glioma patients based on both brain radiomics features from preoperative MRI multi-sequence images and clinical features.
methodsWe retrospectively analyzed the MRI images and clinical data of 388 glioma patients and extracted the radiomics features from the peritumoral edema zone, tumor core, and whole tumor on T1, T2, and T1-weighted contrast-enhanced (T1CE) and fluid attenuated inversion recovery (FLAIR) sequences. The cases were divided into a training set (271 cases) and a test set (117 cases). Random survival forest algorithms were used to select the radiomics features associated with overall survival (OS) in the training set to construct a radiomic score (Rad-score), based on which the patients were classified into high- and low-risk groups for Kaplan-Meier survival analysis. Cox proportional hazard regression models for the 3 different tumor zones were constructed, and their performance for predicting 1- and 3-year survival rates was evaluated using 5-fold cross-validation and AUC analysis followed by external validation using data from another 10 glioma patients. The best-performing model was used for constructing a nomogram for survival predictions.
resultsFive radiomics features from the tumor core, 7 from the peritumoral edema zone, and 5 from the whole tumor were selected. In both the training and test sets, the high- and low-risk groups had significantly different OS (
conclusionThe predictive model combining preoperative multi-modal MRI radiomics features and clinical features can effectively predict survival outcomes of glioma patients.
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