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. 2021 Oct 9;13(20):5047.
doi: 10.3390/cancers13205047.

Predicting Short-Term Survival after Gross Total or Near Total Resection in Glioblastomas by Machine Learning-Based Radiomic Analysis of Preoperative MRI

Santiago Cepeda  1 , Angel Pérez-Nuñez  2 , Sergio García-García  1 , Daniel García-Pérez  2 , Ignacio Arrese  1 , Luis Jiménez-Roldán  2 , Manuel García-Galindo  3 , Pedro González  2 , María Velasco-Casares  4 , Tomas Zamora  5 , Rosario Sarabia  1
Affiliations

Predicting Short-Term Survival after Gross Total or Near Total Resection in Glioblastomas by Machine Learning-Based Radiomic Analysis of Preoperative MRI

Santiago Cepeda et al. Cancers (Basel). .

Abstract

Radiomics, in combination with artificial intelligence, has emerged as a powerful tool for the development of predictive models in neuro-oncology. Our study aims to find an answer to a clinically relevant question: is there a radiomic profile that can identify glioblastoma (GBM) patients with short-term survival after complete tumor resection? A retrospective study of GBM patients who underwent surgery was conducted in two institutions between January 2019 and January 2020, along with cases from public databases. Cases with gross total or near total tumor resection were included. Preoperative structural multiparametric magnetic resonance imaging (mpMRI) sequences were pre-processed, and a total of 15,720 radiomic features were extracted. After feature reduction, machine learning-based classifiers were used to predict early mortality (<6 months). Additionally, a survival analysis was performed using the random survival forest (RSF) algorithm. A total of 203 patients were enrolled in this study. In the classification task, the naive Bayes classifier obtained the best results in the test data set, with an area under the curve (AUC) of 0.769 and classification accuracy of 80%. The RSF model allowed the stratification of patients into low- and high-risk groups. In the test data set, this model obtained values of C-Index = 0.61, IBS = 0.123 and integrated AUC at six months of 0.761. In this study, we developed a reliable predictive model of short-term survival in GBM by applying open-source and user-friendly computational means. These new tools will assist clinicians in adapting our therapeutic approach considering individual patient characteristics.

Keywords: glioblastoma; machine learning; radiomics; survival; texture analysis.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Study workflow. GBM = glioblastoma, GTR = gross-total resection, NTR = near-total resection, BraTS = Brain tumor segmentation challenge 2020, TCIA = The cancer image archive, GLISTRboost = Boosted GLioma Image SegmenTation and Registration, ED = edema, NET = nonenhancing tumor, ET = enhancing tumor, OS = overall survival, InfoGain = Information gain, GINI = Gini Index, FCBF = Fast correlation-based filter, ML = machine learning, AUC = area under the curve, CA = classification accuracy, IBS = integrated Brier score, iAUC = integrated AUC.
Figure 2
Figure 2
Performance assessment of the binary classification model on the testing data set. left: area under the receiver operating characteristic (ROC) curve and right: calibration plot. kNN = k-nearest neighbor, SVM = support vector machine, TP = true positives, FP = false positives, P = positives.
Figure 3
Figure 3
Kaplan–Meier plots showing differences in overall survival for patients in training (A) and testing (B) data sets stratified into low- or high-risk groups by random survival forest (RSF)-based ensemble mortality. Prediction error curves show Cox regression model performance in the training (C) and testing (D) groups. HR= Hazard Ratio, RSF = Random Survival Forest, IBS = Integrated Brier Score, iAUC = Integrated area under the curve, 6m – AUC = six months - area under the curve.
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