Using Deep Learning to Predict Beam-Tunable Pareto Optimal Dose Distribution for Intensity Modulated Radiation Therapy

TL;DR

This paper develops deep learning models to accurately predict Pareto optimal dose distributions in radiation therapy, enabling faster and more automated treatment planning with adjustable beam configurations.

Contribution

The study introduces two novel deep learning models that predict dose distributions based on beam angles and patient anatomy, improving planning efficiency and accuracy.

Findings

Model I achieved lower prediction error than Model II.

Predicted dose distributions closely matched ground truth plans.

Models enable real-time tradeoff adjustments in treatment planning.

Abstract

We propose to develop deep learning models that can predict Pareto optimal dose distributions by using any given set of beam angles, along with patient anatomy, as input to train the deep neural networks. We implement and compare two deep learning networks that predict with two different beam configuration modalities. We generated Pareto optimal plans for 70 patients with prostate cancer. We used fluence map optimization to generate 500 IMRT plans that sampled the Pareto surface for each patient, for a total of 35,000 plans. We studied and compared two different models, Model I and Model II. Model I directly uses beam angles as a second input to the network as a binary vector. Model II converts the beam angles into beam doses that are conformal to the PTV. Our deep learning models predicted voxel-level dose distributions that precisely matched the ground truth dose distributions. Quantitatively, Model I prediction error of 0.043 (confirmation), 0.043 (homogeneity), 0.327 (R50), 2.80% (D95), 3.90% (D98), 0.6% (D50), 1.10% (D2) was lower than that of Model II, which obtained 0.076 (confirmation), 0.058 (homogeneity), 0.626 (R50), 7.10% (D95), 6.50% (D98), 8.40% (D50), 6.30% (D2). Treatment planners who use our models will be able to use deep learning to control the tradeoffs between the PTV and OAR weights, as well as the beam number and configurations in real time. Our dose prediction methods provide a stepping stone to building automatic IMRT treatment planning.