Overall Survival Prediction for Gliomas Using a Novel Compound Approach

Abstract

As a highly malignant tumor, the incidence and mortality of glioma are not optimistic. Predicting the survival time of patients with glioma by extracting the feature information from gliomas is beneficial for doctors to develop more targeted treatments. Magnetic resonance imaging (MRI) is a way to quickly and clearly capture the details of brain tissue. However, manually segmenting brain tumors from MRI will cost doctors a lot of energy, and doctors can only vaguely estimate the survival time of glioma patients, which are not conducive to the formulation of treatment plans. Therefore, automatically segmenting brain tumors and accurately predicting survival time has important significance. In this article, we first propose the NLSE-VNet model, which integrates the Non-Local module and the Squeeze-and-Excitation module into V-Net to segment three brain tumor sub-regions in multimodal MRI. Then extract the intensity, texture, wavelet, shape and other radiological features from the tumor area, and use the CNN network to extract the deep features. The factor analysis method is used to reduce the dimensionality of features, and finally the dimensionality-reduced features and clinical features such as age and tumor grade are combined into the random forest regression model to predict survival. We evaluate the effect on the BraTS 2019 and BraTS 2020 datasets. The average Dice of brain tumor segmentation tasks up to 79% and the average RMSE of the survival predictive task is as low as 311.5. The results indicate that the method in this paper has great advantages in segmentation and survival prediction of gliomas.

Computer-aided diagnosis and prediction in brain disorders

“…One study used a CNN as a pre-trained feature extractor [71]. Other recent approaches using CNNs to extract features that are subsequently combined with other factors into a final prognostic model include [99,46,52]. The 2017/2018 editions of the well-known BraTS challenges also included a task on overall survival prediction, with best teams obtaining accuracies around 0.6 in a three-class classification setting distinguishing short-, mid-, and long-survivors, [8].…”

A Survey of Radiomics in Precision Diagnosis and Treatment of Adult Gliomas

“…Zhang et al [99] constructed a radiomics nomogram for assessing survival in patients with GBMs using a combination of radiomics features extracted from multi-parametric MRI and clinical risk factors, and the result showed that the consistency indices in the training and validation sets were 0.971 and 0.974, respectively. Huang et al [100] used CNN to extract deep features from preoperative MRI of patients with gliomas and performed dimensionality reduction, and then they combined dimensionality reduction features with clinical features such as age and tumor grade to build an RF model for survival prediction, and the result was satisfying.…”

The Efficacy of Shape Radiomics and Deep Features for Glioblastoma Survival Prediction by Deep Learning

“…Combined with the selected radiomics features, the OS prediction result slightly improves. According to [13], deep features are extracted from MRI modalities via a CNN network that describes the tumor's size, shape, and texture. Further, the NLSE model is proposed with the integration of the non-local module and the squeeze-and-excitation module to robust the segmentation results.…”