| Popis: |
PURPOSE Non-invasive and quantitative biomarkers of somatic mutations derived from multi-parametric MRI (MP-MRI) could potentially help in predicting the response of patients to therapy, leading to development of targeted and personalized treatments. In this study, we developed radiogenomic signatures of multiple driver genes using artificial intelligence (AI) methods. METHODS In this study, 2740 radiomic features, including shape and volumetric measures computed for different tumorous regions, and characteristics derived from histograms and gray-level co-occurrence matrix (GLCM), were extracted from pre-operative MP-MRI (T1, T1Gd, T2, T2-FLAIR, DTI, and DSC-MRI) scans of 161 patients with newly diagnosed glioblastoma. The tumor samples, collected surgically from these patients, were sequenced using an in-house targeted next generation sequencing (NGS) panel of genes. We constructed quantitative imaging signatures of somatic mutations in several genes from 161 IDH-wildtype glioblastoma patients, including ATRX, FGFR2, EGFR, MET, NF1, PDGFRA, PIK3CA, PTEN, RB1, TP53, using cross-validated SVM classifiers. RESULTS The cross-validated classification performance for each signature was assessed by area under the receiver operating characteristic (ROC) curve (AUC), indicating the following results: PTEN (n = 69, AUC = 0.64), EGFR (n = 52, AUC = 0.72), TP53 (n = 51, AUC = 0.67), NF1 (n = 33, AUC = 0.74), ATRX (n = 22; AUC = 0.74), FGFR2 (n = 6, AUC = 0.82), MET (n = 26, AUC = 0.77), PDGFRA (n = 14, AUC = 0.82), PIK3CA (n = 14, AUC = 0.78), RB1 (n = 14, AUC = 0.81). CONCLUSION Using multi-parametric MRI, we developed quantitative non-invasive in vivo signatures with the potential for pre-operative assessment of a glioblastoma’s molecular characteristics. These non-invasive radiogenomic biomarkers may be useful for understanding the molecular composition of a glioblastoma prior to surgical resection, thus enabling earlier selection of patients for targeted therapy trials and possible neoadjuvant treatment. |
