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

PURPOSE

This study aimed to develop a deep learning model for the prediction of V (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 V. To evaluate model performance, the coefficient of determination ), 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 V (19.3%; 4.9%-30.7%).

RESULTS

The predictive results of the developed model for V were 0.16, 5.4%, and 4.5% for the , 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 V values was 0.40. As a binary classifier with V <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 V and play an important role in the early determination of patient treatment strategies.