Deep Learning-based Lung dose Prediction Using Chest X-ray Images in Non-small Cell Lung Cancer Radiotherapy.

Abstract

Purpose: This study aimed to develop a deep learning model for the prediction of V₂₀ (the volume of the lung parenchyma that received ≥20 Gy) during intensity-modulated radiation therapy using chest X-ray images.

Methods: The study utilized 91 chest X-ray images of patients with lung cancer acquired routinely during the admission workup. The prescription dose for the planning target volume was 60 Gy in 30 fractions. A convolutional neural network-based regression model was developed to predict V₂₀. To evaluate model performance, the coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE) were calculated with conducting a four-fold cross-validation method. The patient characteristics of the eligible data were treatment period (2018-2022) and V₂₀ (19.3%; 4.9%-30.7%).

Results: The predictive results of the developed model for V₂₀ were 0.16, 5.4%, and 4.5% for the R², RMSE, and MAE, respectively. The median error was -1.8% (range, -13.0% to 9.2%). The Pearson correlation coefficient between the calculated and predicted V₂₀ values was 0.40. As a binary classifier with V₂₀ <20%, the model showed a sensitivity of 75.0%, specificity of 82.6%, diagnostic accuracy of 80.6%, and area under the receiver operator characteristic curve of 0.79.

Conclusions: The proposed deep learning chest X-ray model can predict V₂₀ and play an important role in the early determination of patient treatment strategies.