A radiomics-clinical nomogram for preoperative prediction of IDH1 mutation in primary glioblastoma multiforme

Abstract

Aim: To develop and validate an individualised radiomics-clinical nomogram for the prediction of the isocitrate dehydrogenase 1 (IDH1) mutation status in primary glioblastoma multiforme (GBM) based on radiomics features and clinical variables.

Materials and methods: In a retrospective study, preoperative magnetic resonance imaging (MRI) images were obtained of 122 patients with primary glioblastoma (development cohort = 101; validation cohort = 21). Radiomics features were extracted from total tumour based on the post-contrast high-resolution three-dimensional (3D) T1-weighted MRI images. Radiomics features were selected by using a least absolute shrinkage and selection operator (LASSO) binomial regression model with nested cross-validation. Then, a radiomics-clinical nomogram was constructed by combining relevant radiomics features and clinical variables and subsequently tested by using the independent validation cohort.

Results: A total of 105 features were quantified on the 3D MRI images of each patient, and eight were selected to construct the radiomics model for predicting IDH1 mutation status. The mean classification accuracy and mean κ value achieved with the model were 88.4±3% and 0.701±0.08, respectively. The radiomics-clinical nomogram, which combines eight radiomics features and three clinical variables (patient age, sex and tumour location), demonstrated good discrimination (C-index 0.934 [95% CI, 0.874 to 0.994]; F1 score 0.78) and performed well with the validation cohort (C-index 0.963 [95% CI, 0.957 to 0.969]; F1 score 0.91; AUC 0.956).

Conclusions: A radiomics-clinical nomogram was developed and proved to be valuable in the non-invasive, individualised prediction of the IDH1 mutation status in patients with primary GBM. The nomogram can be applied using clinical conditions to facilitate preoperative patient evaluation.