Generating Pareto Optimal Dose Distributions for Radiation Therapy Treatment Planning

Abstract

Radiotherapy treatment planning currently requires many trial-anderror iterations between the planner and treatment planning system, as well as between the planner and physician for discussion/consultation. The physician's preferences for a particular patient cannot be easily quantified and precisely conveyed to the planner. In this study we present a real-time volumetric Pareto surface dose generation deep learning neural network that can be used after segmentation by the physician, adding a tangible and quantifiable endpoint to portray to the planner. From 70 prostate patients, we first generated 84,000 intensity modulated radiation therapy plans (1,200 plans per patient) sampling the Pareto surface, representing various tradeoffs between the planning target volume (PTV) and the organs-at-risk (OAR), including bladder, rectum, left femur, right femur, and body. We divided the data to 10 test patients and 60 training/validation patients. We then trained a hierarchically densely connected convolutional U-net (HD U-net), to take the PTV and avoidance map representing OARs masks and weights, and predict the optimized plan. The HD U-net is capable of accurately predicting the 3D Pareto optimal dose distributions, with average [mean, max] dose errors of [3.4%, 7.7%](PTV), [1.6%, 5.6%](bladder), [3.7%, 4.2%](rectum), [3.2%, 8.0%](left femur), [2.9%, 7.7%](right femur), and [0.04%, 5.4%](body) of the prescription dose. The PTV dose coverage prediction was also very similar, with errors of 1.3% (D98) and 2.0% (D99). Homogeneity was also similar, differing by 0.06 on average. The neural network can predict the dose within 1.7 seconds. Clinically, the optimization and dose calculation is much slower, taking 5-10 minutes.

Incorporating human and learned domain knowledge into training deep neural networks: A differentiable dose‐volume histogram and adversarial inspired framework for generating Pareto optimal dose distributions in radiation therapy

“…55 In addition to clinical dose prediction, deep learning models are capable of accurately generating Pareto optimal dose distributions, navigating the various tradeoffs between planning target volume (PTV) dose coverage and organs at risk (OAR) dose sparing. 56 Most of these methods utilize a simple loss function for training the neural networkthe mean squared error (MSE) loss. Mean squared error loss is a generalized, domain-agnostic loss function that can be applied to many problems in many domains.…”

Overview of artificial intelligence-based applications in radiotherapy: Recommendations for implementation and quality assurance

“…Other automated planning techniques using DL (e.g. fluence map prediction, multicriteria optimization (MCO), beam orientation optimization are also being described [70][71][72]. In this section, other ways to automate treatment planning, such as scripting or protocol-based iterative planning are not discussed.…”

Fully automated dose prediction using generative adversarial networks in prostate cancer patients

“…In the fairly recent past, researchers have begun to use of deep neural networks (DNN) to predict the dose distribution, engendering a new field of research [4][5][6][7][8][9][10][11][12]. Such dose prediction is useful for confirming the achievable dose distribution before or during the creation of treatment planning, and could reduce the iterative optimization process for IMRT, because the treatment planner can know which areas should receive increased or reduced doses based on the results of the prediction.…”