Artificial Neural Network Modelling Using Pre-Operative MRI Features to Predict Obesity in Pediatric Patients With Craniopharyngioma Treated With Proton Therapy.

Abstract

PURPOSE/OBJECTIVE(S)\nSurvival for children and adolescents diagnosed with craniopharyngioma and treated with surgery and radiation therapy exceeds 90% when measured at 10 years. Quality of life is often impacted by obesity resulting from tumor and treatment. We hypothesized that obesity could be predicted by analyzing pre-operative magnetic resonance imaging (MRI) radiomic features. An artificial neural network (ANN) model was developed to predict increase in body mass index (BMI) after surgery and proton therapy using MRI features.\n\n\nMATERIALS/METHODS\nEighty-four patients diagnosed with craniopharyngioma (aged 1-20 years) were included in this study. Surgery and proton therapy were performed on a prospective therapeutic trial. Institutional review board approval was obtained for this retrospective study. BMI was measured every 3 months in the first follow-up year and every 6 months hereafter until 5 years after therapy. The endpoint of this study was defined as the percentile increase of the normalized BMI in the last follow-up year. Radiomics features were extracted from pre-operative MRI images with T1-, T2-weighted, and FLAIR sequences using radiomics software. Images were pre-processed to improve the uniformity of the multicenter MRI datasets. One hundred and five features were calculated, and 5 key features were selected using an embedded feature selection method. A 4-layer ANN with group-Lasso and dropout regularization was developed to predict BMI increase using TensorFlow (Google, USA) toolbox. Patients were divided into 3 groups based on their BMI percentile increase from pre-irradiation baseline with thresholds of 10% and 20%, respectively. Data augmentation and 5-fold cross-validation were used to improve the stability of the ANN model.\n\n\nRESULTS\nThe average accuracy of the testing dataset in the 5-fold cross validation was 82.3% ± 4.3%, 69.1% ± 3.0%, and 84.0% ± 5.0% for T1, T2, and FLAIR images, respectively. The macro average area under the ROC curve (AUC) was 0.92, 0.88, and 0.92 for T1, T2, and FLAIR images, respectively. The average confusion matrix principal elements were 0.55, 1, and 0.92 for the 3 groups in T1 images, 0.29, 0.96, and 0.82 in T2 images, and 0.59, 0.98, and 0.96 in FLAIR images.\n\n\nCONCLUSION\nA predictive model of post-treatment BMI increase for pediatric patients with craniopharyngioma was developed with relatively high accuracy and AUC. This model could facilitate early identification of patients at risk for obesity after treatment for close surveillance and intervention.