Glioblastoma Multiforme Patient Survival Prediction

TL;DR

This paper develops survival prediction models for glioblastoma multiforme using handcrafted image and radiomics features, demonstrating that shape features with gradient boosting yield high accuracy and outperform previous challenge winners.

Contribution

It introduces a novel combination of radiomics shape features and gradient boosting regressors for improved glioblastoma survival prediction.

Findings

Gradient boosting with shape features achieved up to 91.5% accuracy on training data.

The proposed method outperforms previous winners of the BraTS 2020 challenge.

Handcrafted radiomics features show strong correlation with patient survival.

Abstract

Glioblastoma Multiforme is a very aggressive type of brain tumor. Due to spatial and temporal intra-tissue inhomogeneity, location and the extent of the cancer tissue, it is difficult to detect and dissect the tumor regions. In this paper, we propose survival prognosis models using four regressors operating on handcrafted image-based and radiomics features. We hypothesize that the radiomics shape features have the highest correlation with survival prediction. The proposed approaches were assessed on the Brain Tumor Segmentation (BraTS-2020) challenge dataset. The highest accuracy of image features with random forest regressor approach was 51.5\% for the training and 51.7\% for the validation dataset. The gradient boosting regressor with shape features gave an accuracy of 91.5\% and 62.1\% on training and validation datasets respectively. It is better than the BraTS 2020 survival prediction challenge winners on the training and validation datasets. Our work shows that handcrafted features exhibit a strong correlation with survival prediction. The consensus based regressor with gradient boosting and radiomics shape features is the best combination for survival prediction.