An Interpretable Web-based Glioblastoma Multiforme Prognosis Prediction Tool using Random Forest Model

TL;DR

This paper presents an interpretable web-based prognostic tool for glioblastoma multiforme using machine learning models, achieving competitive accuracy and highlighting key prognostic factors, with a focus on clinical interpretability and medical knowledge consistency.

Contribution

It introduces the first interpretable GBM prognosis prediction models that incorporate medical knowledge and use machine learning techniques like random forests and survival models.

Findings

Best AUROC for classification: 0.7076

Highest C-index for survival: 0.7157

Key prognostic factors identified: MGMT gene, resection extent, age

Abstract

We propose predictive models that estimate GBM patients' health status of one-year after treatments (Classification task), predict the long-term prognosis of GBM patients at an individual level (Survival task). We used total of 467 GBM patients' clinical profile consists of 13 features and two follow-up dates. For baseline models of random forest classifier(RFC) and random survival forest model (RSF), we introduced generalized linear model (GLM), support vector machine (SVM) and Cox proportional hazardous model (COX), accelerated failure time model (AFT) respectively. After preprocessing and prefixing stratified 5-fold data set, we generated best performing models for model types using recursive feature elimination process. Total 10, 4, and 13 features were extracted for best performing one-year survival/progression status RFC models and RSF model via the recursive feature elimination process. In classification task, AUROC of best performing RFC recorded 0.6990 (for one-year survival status classification) and 0.7076 (for one-year progression classification) while that of second best baseline models (GLM in both cases) recorded 0.6691 and 0.6997 respectively. About survival task, the highest C-index of 0.7157 and the lowest IBS of 0.1038 came from the best performing RSF model while that of second best baseline models were 0.6556 and 0.1139 respectively. A simplified linear correlation (extracted from LIME and virtual patient group analysis) between each feature and prognosis of GBM patient were consistent with proven medical knowledge. Our machine learning models suggest that the top three prognostic factors for GBM patient survival were MGMT gene promoter, the extent of resection, and age. To the best of our knowledge, this study is the very first study introducing a interpretable and medical knowledge consistent GBM prognosis predictive models.