A machine learning‐based survival prediction model of high grade glioma by integration of clinical and dose‐volume histogram parameters. (24th March 2021)

• Record Type: Journal Article
• Authors: Chen, Haiyan; Li, Chao; Zheng, Lin; Lu, Wei; Li, Yanlin; Wei, Qichun
• Is Part Of: Cancer medicine. Volume 10:Number 8(2021)
• Page Start: 2774
• Page End: 2786
• Publication Date: 2021-03-24
• Subjects: DVH features -- high‐grade glioma -- machine learning -- random survival forest -- survival prediction; 616.994005
• DOI: 10.1002/cam4.3838
• Languages: English
• ISSNs: 2045-7634

Abstract

Abstract: Purpose: Glioma is the most common type of primary brain tumor in adults, and it causes significant morbidity and mortality, especially in high‐grade glioma (HGG) patients. The accurate prognostic prediction of HGG is vital and helpful for clinicians when developing therapeutic strategies. Therefore, we propose a machine learning‐based survival prediction model by analyzing clinical and dose‐volume histogram (DVH) parameters, to improve the performance of the risk model in HGG patients. Methods: Eight clinical variables and 39 DVH parameters were extracted for each patient, who received radiotherapy for HGG with active follow‐up. Ninety‐five patients were randomly divided into training and testing cohorts, and we employed random survival forest (RSF), support vector machine (SVM), and Cox proportional hazards (CPHs) models to predict survival. Calibration plots, concordance indexes, and decision curve analyses were used to evaluate the calibration, discrimination, and clinical utility of these three models. Results: The RSF model showed the best performance among the three models, with concordance indexes of 0.824 and 0.847 in the training and testing sets, respectively, followed by the SVM (0.792/0.823) and CPH (0.821/0.811) models. Specifically, in the RSF model, we identified age, gross tumor volume (GTV), grade, Karnofsky performance status (KPS), isocitrate dehydrogenase (IDH), and D99 as important variables associated with survival. The AUCs of the testing set were 92.4%, 87.7%, and 84.0% for 1‐, 2‐, and 3‐year survival, respectively. According to this model, HGG patients can be divided into high‐ and low‐risk groups. Conclusion: The machine learning‐based RSF model integrating both clinical and DVH variables is an improved and useful tool for predicting the survival of HGG patients. Abstract : We propose the machine learning‐based RSF and SVM models, and the classical CPH model, to identify predictors for survival and examine treatment outcomes in patients with HGG by integrating clinical and DVH parameters.The RSF model showed the best performance among the three models and is an improved and useful tool for the survival prediction of HGG.