Predictive value of magnetic resonance imaging radiomics-based machine learning for disease progression in patients with high-grade glioma

Quantitative Imaging in Medicine and Surgery

article

Zhibin Li¹  Li Chen³  Ying Song¹  Guyu Dai¹  Lian Duan⁴  Yong Luo⁵  Guangyu Wang¹  Qing Xiao¹  Guangjun Li¹  Sen Bai¹ 

[1] Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital , Sichuan University;Department of Radiation Oncology , The First Affiliated Hospital of Soochow University;Department of Radiotherapy & Oncology, The Second Affiliated Hospital of Soochow University, Institute of Radiotherapy & Oncology , Soochow University;Department of Radiation Oncology, Perelman School of Medicine , University of Pennsylvania;Department of Head & Neck Oncology, West China Hospital , Sichuan University

关键词: High-grade glioma (HGG);    radiotherapy;    prognosis prediction;    radiomics;    magnetic resonance imaging (MRI);   

DOI  :  10.21037/qims-22-459

学科分类：外科医学

来源: AME Publications

【 摘 要 】

Background: Accurately predicting the prognosis of patients with high-grade glioma (HGG) is potentially important for treatment. However, the predictive value of images of various magnetic resonance imaging (MRI) sequences for prognosis at different time points is unknown. We established predictive machine learning models of HGG disease progression and recurrence using MRI radiomics and explored the factors influencing prediction accuracy. Methods: Radiomics features were extracted from T1-weighted (T1WI), contrast-enhanced T1-weighted (CE-T1WI), T2-weighted (T2WI), and fluid-attenuated inversion recovery (FLAIR) images (postoperative radiotherapy planning MRI images) obtained from 162 patients with HGG. The Mann-Whitney U test and least absolute shrinkage and selection operator (LASSO) algorithm were used for feature selection. Machine learning models were used to build prediction models to estimate disease progression or recurrence. The influence of different MRI sequences, regions of interest (ROIs), and prediction time points was also explored. The receiver operating characteristic (ROC) curve was used to evaluate the discriminative performance of each model, and the DeLong test was employed to compare the ROC curves. Results: Radiomics features from T2WI and FLAIR demonstrated greater predictive value for disease progression compared with T1WI or CE-TIWI. The best predictive models, with areas under the ROC curves (AUCs) of 0.70, 0.68, 0.78, 0.78, and 0.78 for predicting disease progression at the 6th, 9th, 12th, 15th, and 18th month after radiotherapy, respectively, were obtained by combining clinical features with gross tumor volume (GTV) and clinical target volume (CTV) features extracted from T2WI and FLAIR. Conclusions: Structural MRI obtained before radiotherapy can be used to predict the disease progression or posttreatment recurrence of HGG. When using MRI radiomics to predict long-term outcomes as opposed to short-term outcomes, better predictive results may be obtained.

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