A Deep Learning Model for Predicting Xerostomia Due to Radiation Therapy for Head and Neck Squamous Cell Carcinoma in the RTOG 0522 Clinical Trial

Abstract

Xerostomia commonly occurs in patients who undergo head and neck radiation therapy and can seriously affect patients' quality of life. In this study, we developed a xerostomia prediction model with radiation treatment data using a 3-dimensional (3D) residual convolutional neural network (rCNN). The model can be used to guide radiation therapy to reduce toxicity. A total of 784 patients with head and neck squamous cell carcinoma enrolled in the Radiation Therapy Oncology Group 0522 clinical trial were included in this study. Late xerostomia is defined as xerostomia of grade ≥2 occurring in the 12th month of radiation therapy. 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 1 of the 3 inputs and for the logistic regression model. Accuracy, sensitivity, specificity, F-score, and area under the receiver operator characteristic curve were evaluated. The proposed model achieved promising prediction results. The performance metrics for 3D rCNN model with contour, CT images, and radiation therapy 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 0.75; specificity: 0.76, 0.76, 0.71, 0.69, 0.59, and 0.43; F-score: 0.70, 0.68, 0.69, 0.56, 0.60, and 0.57; and area under the receiver operator characteristic curve: 0.84, 0.82, 0.78, 0.70, 0.74, and 0.68, respectively. The proposed model uses 3D rCNN filters to extract low- and high-level spatial features and to achieve promising performance. This is a potentially effective model for predicting objective toxicity for supporting clinical decision making.