Qingrong Wu

PURPOSE: To develop a tradeoff hyperplane model to facilitate tradeoff decision-making before inverse planning. METHODS AND MATERIALS: We propose a model-based approach to determine the tradeoff hyperplanes that allow physicians to navigate the clinically viable space of plans with best achievable dose-volume parameters before planning. For a given case, a case reference set (CRS) is selected using a novel anatomic similarity metric from a large reference plan pool. Then, a regression model is built on the CRS to estimate the expected dose-volume histograms (DVHs) for the current case. This model also predicts the DVHs for all CRS cases and captures the variation from the corresponding DVHs in the clinical plans. Finally, these DVH variations are analyzed using the principal component analysis to determine the tradeoff hyperplane for the current case. To evaluate the effectiveness of the proposed approach, 244 head and neck cases were randomly partitioned into reference (214) and validation (30) sets. A tradeoff hyperplane was built for each validation case and evenly sampled for 12 tradeoff predictions. Each prediction yielded a tradeoff plan. The root-mean-square errors of the predicted and the realized plan DVHs were computed for prediction achievability evaluation. RESULTS: The tradeoff hyperplane with 3 principal directions accounts for 57.8% ± 3.6% of variations in the validation cases, suggesting the hyperplanes capture a significant portion of the clinical tradeoff space. The average root-mean-square errors in 3 tradeoff directions are 5.23 ± 2.46, 5.20 ± 2.52, and 5.19 ± 2.49, compared with 4.96 ± 2.48 of the knowledge-based planning predictions, indicating that the tradeoff predictions are comparably achievable. CONCLUSIONS: Clinically relevant tradeoffs can be effectively extracted from existing plans and characterized by a tradeoff hyperplane model. The hyperplane allows physicians and planners to explore the best clinically achievable plans with different organ-at-risk sparing goals before inverse planning and is a natural extension of the current knowledge-based planning framework.

© 2019 IEEE. This study aims to demonstrate the feasibility of using a novel distance-based representation of 3D CT-scan images to train a deep learning model for dose predictions in radiation treatment planning. The distance representation is inspired by previous knowledge of the domain to increase the generalizability of the deep learning models for radiation treatment planning. Conventional knowledge-based planning methods extract engineered features from 3D CT-scan images, as well as other patients' features, to predict the best achievable dose in a cancerous area and other organs at risk. Recent studies have shown higher accuracy in voxel-level dose prediction using deep learning models compared to the conventional machine learning approaches. Since the data resources for training these models are limited, most of the studies use 2D contour information to represent the patient anatomy. This representation loses volumetric information, and it is sensitive to small changes in patient orientation and translation. The distance-based representation introduced in this paper is inspired by the domain knowledge and is able to maintain the volumetric distance information despite the 2D slicing of 3D CT-image. According to prior studies in the radiation treatment planning domain, there is a strong association between the organs-at-risk distance from the cancerous volume and the patient's vulnerability to receive excessive dose. Therefore, the contour value in prior representation was replaced by voxel distance from cancerous volume. This modification in representation makes it transition and orientation invariant and adds potential robustness to patient positioning differences during the imaging/planning process. We evaluated the distance-based deep learning models through experiments for prediction of prostate cancer patients' vulnerability and voxel-level dose distribution using convolutional neural network and U-net models, respectively. The results were compared with contour-based U-net model as well as conventional machine learning with engineered representations. We found that the performance was comparable or higher than the prior state-of-the-art results for prostate-cancer dose distribution prediction.