Multiregional radiomics features from multiparametric MRI for prediction of MGMT methylation status in glioblastoma multiforme: A multicentre study

• Published in: European radiology 2018-09, Vol.28 (9), p.3640-3650
• Main Authors: Li, Zhi-Cheng, Bai, Hongmin, Sun, Qiuchang, Li, Qihua, Liu, Lei, Zou, Yan, Chen, Yinsheng, Liang, Chaofeng, Zheng, Hairong
• Format: Article
• Language: English
• Subjects: Adolescent; Adult; Aged; Aged, 80 and over; Biomarkers; Biomarkers, Tumor - genetics; Brain cancer; Brain Neoplasms - diagnostic imaging; Brain Neoplasms - genetics; Child; Computer Applications; Construction; Deoxyribonucleic acid; Diagnostic Radiology; DNA; DNA Methylation; DNA methyltransferase; DNA Modification Methylases - genetics; DNA Repair Enzymes - genetics; DNA, Neoplasm - genetics; Feature extraction; Female; Glioblastoma; Glioblastoma - diagnostic imaging; Glioblastoma - genetics; Humans; Imaging; Internal Medicine; Interventional Radiology; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Mathematical models; Medicine; Medicine & Public Health; Methylguanine; Middle Aged; Model accuracy; Neuroradiology; NMR; Nuclear magnetic resonance; O6-methylguanine-DNA methyltransferase; Patients; Performance prediction; Prediction models; Promoter Regions, Genetic; Radiology; Radiomics; Redundancy; Retrospective Studies; ROC Curve; Subgroups; Tumor Suppressor Proteins - genetics; Ultrasound; Young Adult
• ISSN: 0938-7994; 1432-1084
• DOI: 10.1007/s00330-017-5302-1

Objectives To build a reliable radiomics model from multiregional and multiparametric magnetic resonance imaging (MRI) for pretreatment prediction of O 6 -methylguanine-DNA methyltransferase (MGMT) promotor methylation status in glioblastoma multiforme (GBM). Methods In this retrospective multicentre study, 1,705 multiregional radiomics features were automatically extracted from multiparametric MRI. A radiomics model with a minimal set of all-relevant features and a radiomics model with univariately-predictive and non-redundant features were built for MGMT methylation prediction from a primary cohort (133 patients) and tested on an independent validation cohort (60 patients). Predictive models combing clinical factors were built and evaluated. Both radiomics models were assessed on subgroups stratified by clinical factors. Results The radiomics model with six all-relevant features allowed pretreatment prediction of MGMT methylation (AUC=0.88, accuracy=80 %), which significantly outperformed the model with eight univariately-predictive and non-redundant features (AUC=0.76, accuracy=70 %). Combing clinical factors with radiomics features did not benefit the prediction performance. The all-relevant model achieved significantly better performance in stratified analysis. Conclusions Radiomics model built from multiregional and multiparameter MRI may serve as a potential imaging biomarker for pretreatment prediction of MGMT methylation in GBM. The all-relevant features have the potential of offering better predictive power than the univariately-predictive and non-redundant features. Key Points • Multiregional and multiparametric MRI features reliably predicted MGMT methylation in multicentre cohorts. • All-relevant imaging features predicted MGMT methylation better than univariately-predictive and non-redundant features. • Combing clinical factors with radiomics features did not benefit the prediction performance.