Predicting the need for a replan in oropharyngeal cancer: a radiomic, clinical, and dosimetric model

Abstract

Patients with oropharyngeal cancer (OPC) treated with chemoradiation experience weight loss and tumor shrinkage. As a result, many of these patients will require a replan during radiation treatment. We aimed to develop a machine learning model to predict the need for a replan in patients with OPC (n=315). A total of 78 patients (25%) required a replan. The dataset was split into independent training (n=220) and testing (n=95) datasets. Tumor volumes and organs at risk (OARs) were contoured on planning CT images prior to treatment. PyRadiomics was used to compute radiomic features from the primary tumor, nodal volumes, and parotid glands. Clinical and dose features extracted from the OARs were collected and those significantly associated with the need for a replan in the training dataset were used in a baseline model. Feature selection was applied to select the optimal radiomic features. Classifiers were built using the non-correlated selected radiomic, clinical, and dose features on the training dataset and performance was assessed in the testing dataset. Three clinical and one dose feature were incorporated into the baseline model, as well as into the combined models. Eight predictive radiomic features were selected. The baseline model achieved an AUC of 0.66 [95% CI: 0.51-0.79] in the testing dataset. The Naïve Bayes was the top-performing radiomics model and achieved an AUC of 0.80 [95% CI: 0.69-0.90] in the testing dataset, outperforming the baseline model (p=0.005). This model could assist physicians in identifying patients who may benefit from a replan, improving the replanning workflow.