MRI-based Radiomics Model Can Improve the Predictive Performance of Postlaminar Optic Nerve Invasion in Retinoblastoma

Abstract Purpose: To develop an MRI-based radiomics model to predict postlaminar optic nerve invasion (PLONI) in retinoblastoma (RB) and to compare its predictive performance with that of subjective radiologists’ assessment.Methods: We retrospectively enrolled 124 patients with pathologically proven RB (90 in the training set and 34 in the validation set) who had MRI scans before surgery in this retrospective study. A radiomics model for predicting PLONI was developed by extracting 2058 quantitative imaging features from axial T2-weighted images and contrast-enhanced T1-weighted images in the training set. The Kruskal-Wallis test, least absolute shrinkage and selection operator regression, and recursive feature elimination were used for feature selection, whereupon a radiomics model was built with a logistic regression (LR) classifier. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve and the accuracy were assessed to evaluate the predictive performance of PLONI in the training set and validation set. The performance of the radiomics model was compared to radiologists’ assessment.Results: The AUC of the radiomics model for the prediction of PLONI according to ROC analysis was 0.928 in the training set and 0.841 in the validation set. In all 124 patients, the AUC of the radiomics model was 0.897, while that of radiologists’ assessment was 0.674 (p< 0.001).Conclusions: By incorporating MRI-based radiomics features, we constructed a radiomics model to predict PLONI in patients with RB, and it was shown to be superior to visual assessment and may serve as a potential tool to guide personalized treatment.