Prediction of Radiation Pneumonitis Using Deep Learning Applied to Dose–Function Metrics
Abstract
Purpose
Predicting radiation pneumonitis (RP) using machine learning is promising, particularly when functional lung heterogeneity is incorporated via dose-function histogram (DFH) and CT ventilation imaging (CTVI). However, traditional dose–function metrics often fail to capture complex, high-dimensional spatial relationships between radiation dose and regional lung function. This study aims to improve RP prediction accuracy by developing a deep learning model utilizing multichannel 3D inputs, integrating both CTVI and 3D dose distributions.
Methods
We retrospectively analyzed 94 patients with non-small cell lung cancer (Stages IB–IIIC) who underwent definitive radiotherapy; 23 patients (24.5%) developed grade ≥2 RP. Dual-channel 3D input volumes were constructed by mapping CTVI (functional channel) and dose distribution (dosimetric channel) to the separate channels of a 3D ResNet-18 architecture. Single-channel models using only dose distributions were evaluated for comparison. Transfer learning was implemented using two fine-tuning strategies: updating only the fully connected (FC) layer and updating Layer 4 through the FC layer. Robustness was ensured using stratified 5-fold cross-validation, randomly repeated 50 times. Performance was evaluated using the mean area under the receiver operating characteristic curve (AUC) and DeLong test.
Results
The dual-channel model (fine-tuned FC layer) achieved the highest performance, with a mean AUC of 0.997 (95% CI: 0.982–1.00). The alternative dual-channel fine-tuning strategy yielded a comparable mean AUC of 0.996 (95% CI: 0.990–1.00). In contrast, the single-channel models achieved significantly worse results, with a mean AUC of 0.963 (95% CI: 0.941–0.984), regardless of the fine-tuning strategy. The superiority of the dual channel approach was statistically significant (p < 0.001).
Conclusion
Integrating regional lung function and 3D dose distributions into a multichannel deep learning framework yielded exceptional predictive accuracy for RP. Therefore, this approach significantly enhances RP prediction, potentially contributing to personalized radiotherapy.