Purpose: To introduce a novel moment-based loss function for predicting 3D dose distribution for the challenging conventional lung IMRT plans. The moment-based loss function is convex and differentiable and can easily incorporate clinical dose volume histogram (DVH) domain knowledge in any deep learning framework without computational overhead.
Methods: We used a large dataset of 360 (240 for training, 50 for validation and 70 for testing) challenging conventional lung patients with 2Gy x 30 fractions to train the deep learning (DL) model using clinically treated plans at our institution. We trained a UNet like CNN architecture using computed tomography (CT), planning target volume (PTV) and organ-at-risk contours (OAR) as input to infer corresponding voxel-wise 3D dose distribution. We evaluated three different loss functions: 1) Mean Absolute Error (MAE) Loss 2) MAE Loss + DVH Loss 3) MAE Loss + Moments Loss. The quality of the predictions was compared using different DVH metrics as well as dose-score and DVH-score, recently introduced by the AAPM knowledge-based planning grand challenge.
Results: Model with MAE + Moment loss function outperformed the MAE and MAE+DVH loss with DVH-score of 2.66 ± 1.40 compared to DVH-score of 2.88 ± 1.39 and 2.79 ± 1.52 for the other two respectively. Model with MAE+Moment loss also converged twice as fast as MAE+DVH loss, with training time of ~7 hours compared to ~14 hours for MAE+DVH Loss. Sufficient improvement was found in D95 and D99 dose prediction error for PTV with better predictions for mean/max dose for OARs specially cord and esophagus.
Conclusion: DVH metrics are widely accepted evaluation criteria in the clinic. However, incorporating them into the 3D dose prediction model is challenging due to their non-convexity and non-differentiability. Moments provide a mathematically rigorous and computationally efficient way to incorporate DVH information in any deep learning architecture.
Funding Support, Disclosures, and Conflict of Interest: This work was partially supported by the MSK Cancer Center Support Grant/Core Grant (NIH P30 CA008748)
Radiation Therapy, Dose Volume Histograms
IM/TH- Mathematical/Statistical Foundational Skills: Machine Learning