A deep learning model for predicting xerostomia due to radiotherapy for head-and-neck squamous cell carcinoma in the RTOG 0522 clinical trial
Abstract
Purpose: Xerostomia commonly occurs in patients who undergo head-and-neck radiotherapy and can seriously affect patients’ quality of life. In this study, we developed a xerostomia prediction model with radiation treatment data using a three-dimensional residual convolutional neural network (3D rCNN). The model can be used to guide radiotherapy to reduce toxicity. Methods and Materials: A total of 784 patients with head-and-neck squamous cell carcinoma enrolled in the RTOG 0522 clinical trial were included in this study. Late xerostomia is defined as xerostomia of ≥grade 2 occurring in the 12th month of radiotherapy. The computed tomography (CT) planning images, 3D dose distributions, and contours of the parotid and submandibular glands were included as 3D rCNN inputs. Comparative experiments were performed for the 3D rCNN model without one of the three inputs and for the logistic regression model. Accuracy, sensitivity, specificity, F-score, and area under the receiver operator characteristic curve (AUC) were evaluated. Results: The proposed model achieved promising prediction results. The performance metrics for 3D rCNN model with contour, CT images, and radiotherapy dose; 3D rCNN without contour; 3D rCNN without CT images; 3D rCNN without the dose; logistic regression with the dose and clinical parameters; and logistic regression without clinical parameters were as follows: accuracy: 0.76, 0.74, 0.73, 0.65, 0.64, and 0.56; sensitivity: 0.76, 0.72, 0.77, 0.59, 0.72, and…
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