Preoperative prediction of MGMT promoter methylation in glioblastoma based on multiregional and multi-sequence MRI radiomics analysis

• Published in: Scientific reports 2024-07, Vol.14 (1), p.16031-12, Article 16031
• Main Authors: Li, Lanqing, Xiao, Feng, Wang, Shouchao, Kuang, Shengyu, Li, Zhiqiang, Zhong, Yahua, Xu, Dan, Cai, Yuxiang, Li, Sirui, Chen, Jun, Liu, Yaou, Li, Junjie, Li, Huan, Xu, Haibo
• Format: Article
• Language: English
• Subjects: 631/114/1305; 631/114/1564; 631/67/1922; 631/67/2321; 692/53/2423; Adult; Aged; Brain Neoplasms - diagnostic imaging; Brain Neoplasms - genetics; Brain Neoplasms - pathology; DNA Methylation; DNA methyltransferase; DNA Modification Methylases - genetics; DNA Repair Enzymes - genetics; Female; Glioblastoma; Glioblastoma - diagnostic imaging; Glioblastoma - genetics; Glioblastoma - pathology; Glioma; Humanities and Social Sciences; Humans; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Methylation; Methylguanine; Middle Aged; multidisciplinary; Nucleotide sequence; O6-methylguanine-DNA methyltransferase; Prognosis; Promoter Regions, Genetic; Radiomics; Retrospective Studies; ROC Curve; Science; Science (multidisciplinary); Tumor Suppressor Proteins - genetics
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-66653-2

O6-methylguanine-DNA methyltransferase (MGMT) has been demonstrated to be an important prognostic and predictive marker in glioblastoma (GBM). To establish a reliable radiomics model based on MRI data to predict the MGMT promoter methylation status of GBM. A total of 183 patients with glioblastoma were included in this retrospective study. The visually accessible Rembrandt images (VASARI) features were extracted for each patient, and a total of 14676 multi-region features were extracted from enhanced, necrotic, “non-enhanced, and edematous” areas on their multiparametric MRI. Twelve individual radiomics models were constructed based on the radiomics features from different subregions and different sequences. Four single-sequence models, three single-region models and the combined radiomics model combining all individual models were constructed. Finally, the predictive performance of adding clinical factors and VASARI characteristics was evaluated. The ComRad model combining all individual radiomics models exhibited the best performance in test set 1 and test set 2, with the area under the receiver operating characteristic curve (AUC) of 0.839 (0.709–0.963) and 0.739 (0.581–0.897), respectively. The results indicated that the radiomics model combining multi-region and multi-parametric MRI features has exhibited promising performance in predicting MGMT methylation status in GBM. The Modeling scheme that combining all individual radiomics models showed best performance among all constructed moels.