Non‐invasive prediction of overall survival time for glioblastoma multiforme patients based on multimodal MRI radiomics

• Published in: International journal of imaging systems and technology 2023-07, Vol.33 (4), p.1261-1274
• Main Authors: Zhu, Jingyu, Ye, Jianming, Dong, Leshui, Ma, Xiaofei, Tang, Na, Xu, Peng, Jin, Wei, Li, Ruipeng, Yang, Guang, Lai, Xiaobo
• Format: Article
• Language: English
• Subjects: Brain; Brain cancer; Customization; deep learning; glioblastoma multiforme; Heterogeneity; Magnetic resonance imaging; Medical imaging; overall survival time; Radiomics; Regression models; Segments; Support vector machines; Survival; Three dimensional models; Tumors
• ISSN: 0899-9457; 1098-1098; 0899-9457
• DOI: 10.1002/ima.22869

Glioblastoma multiforme (GBM) is the most common and deadly primary malignant brain tumor. As GBM tumor is aggressive and shows high biological heterogeneity, the overall survival (OS) time is extremely low even with the most aggressive treatment. If the OS time can be predicted before surgery, developing personalized treatment plans for GBM patients will be beneficial. Magnetic resonance imaging (MRI) is a commonly used diagnostic tool for brain tumors with high‐resolution and sound imaging effects. However, in clinical practice, doctors mainly rely on manually segmenting the tumor regions in MRI and predicting the OS time of GBM patients, which is time‐consuming, subjective and repetitive, limiting the effectiveness of clinical diagnosis and treatment. Therefore, it is crucial to segment the brain tumor regions in MRI, and an accurate pre‐operative prediction of OS time for personalized treatment is highly desired. In this study, we present a multimodal MRI radiomics‐based automatic framework for non‐invasive prediction of the OS time for GBM patients. A modified 3D‐UNet model is built to segment tumor subregions in MRI of GBM patients; then, the radiomic features in the tumor subregions are extracted and combined with the clinical features input into the Support Vector Regression (SVR) model to predict the OS time. In the experiments, the BraTS2020, BraTS2019 and BraTS2018 datasets are used to evaluate our framework. Our model achieves competitive OS time prediction accuracy compared to most typical approaches.