MRI-based machine learning for determining quantitative and qualitative characteristics affecting the survival of glioblastoma multiforme

Abstract

Purpose: Our current study aims to consider the image biomarkers extracted from the MRI images for exploring their effects on glioblastoma multiforme (GBM) patients' survival. Determining its biomarker helps better manage the disease and evaluate treatments. It has been proven that imaging features could be used as a biomarker. The purpose of this study is to investigate the features in MRI and clinical features as the biomarker association of survival of GBM.

Methods: 55 patients were considered with five clinical features, 10 qualities pre-operative MRI image features, and six quantitative features obtained using BraTumIA software. It was run ANN, C5, Bayesian, and Cox models in two phases for determining important variables. In the first phase, we selected the quality features that occur at least in three models and quantitative in two models. In the second phase, models were run with the extracted features, and then the probability value of variables in each model was calculated.

Results: The mean of accuracy, sensitivity, specificity, and area under curve (AUC) after running four machine learning techniques were 80.47, 82.54, 79.78, and 0.85, respectively. In the second step, the mean of accuracy, sensitivity, specificity, and AUC were 79.55, 78.71, 79.83, and 0.87, respectively.

Conclusion: We found the largest size of the width, the largest size of length, radiotherapy, volume of enhancement, volume of nCET, satellites, enhancing margin, and age feature are important features.

Keywords: Biomarker; Clinical features; Glioblastoma multiforme; MRI features; Machine learning.