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.
Patients with oropharyngeal cancer (OPC) treated with chemoradiation suffer treatment-related toxicities which can lead to nutritional deficiencies and weight loss. As a result, many of these patients will require supportive care interventions, such as a feeding tube. We aimed to develop a machine learning model to predict feeding tube insertion in patients with OPC (n=343). A total of 116 patients (34%) required a feeding tube. Primary gross tumor volumes were contoured on planning CT images for patients prior to treatment. PyRadiomics was used to compute 1212 radiomic features from these volumes on the original and filtered images. The dataset was split into independent training (n=244) and testing (n=99) datasets. LASSO feature selection was applied to select the optimal features to predict feeding tube insertion. Support vector machine (SVM) and random forest (RF) classifiers were built using the selected features on the training dataset. The machine learning models’ performances were assessed in the testing dataset based on the metric of the AUC. Through feature selection, seven predictive features were selected. This included one original texture, two filtered first order, three filtered texture, and one clinical feature. The top performing classifier was the RF model which achieved an AUC of 0.69 [95% CI: 0.57-0.80] in the testing dataset. To the best of our knowledge, this is the first study to use radiomics to predict feeding tube insertion. This model could assist physicians in identifying patients who may benefit from prophylactic feeding tube insertion, ultimately improving quality of life for patients with OPC.
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