Using deep learning to predict beam‐tunable Pareto optimal dose distribution for intensity‐modulated radiation therapy

Abstract

Purpose: Many researchers have developed deep learning models for predicting clinical dose distributions and Pareto optimal dose distributions. Models for predicting Pareto optimal dose distributions have generated optimal plans in real time using anatomical structures and static beam orientations. However, Pareto optimal dose prediction for intensity-modulated radiation therapy (IMRT) prostate planning with variable beam numbers and orientations has not yet been investigated. We propose to develop a deep learning model 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. Methods: 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, Models I and II. Although they both used the same anatomical structuresincluding the planning target volume (PTV), organs at risk (OARs), and bodythese models were designed with two different methods for representing beam angles. 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. We divided the 70 patients into 54 training, 6 validation, and 10 testing patients, thus yielding 27 000 training, 3000 validation, and 5000 testing plans. Mean square loss (MSE) was taken as the loss function. We used the Adam optimizer with a default learning rate of 0.01 to optimize the network's performance. We evaluated the models' performance by comparing their predicted dose distributions with the ground truth (Pareto optimal) dose distribution, in terms of dose volume histogram (DVH) plots and evaluation metrics such as PTV D 98 , D 95 , D 50 , D 2 , D max , D mean , Paddick Conformation Number, R50, and Homogeneity index. Results: Our deep learning models predicted voxel-level dose distributions that precisely matched the ground truth dose distributions. The DVHs generated also precisely matched the ground truth. Evaluation metrics such as PTV statistics, dose conformity, dose spillage (R50), and homogeneity index also confirmed the accuracy of PTV curves on the DVH. Quantitatively, Model I's prediction error of 0.043 (confirmation), 0.043 (homogeneity), 0.327 (R50), 2.80% (D95), 3.90% (D98), 0.6% (D50), and 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), and 6.30% (D2). Model I also outperformed Model II in terms of the mean dose error and the max dose error on the PTV, bladder, rectum, left femoral head, and right femoral head. Conclusions: Treatment planners who use our models will be able to use deep learning to control the trade-offs between the PTV and OAR...

Knowledge‐based radiation treatment planning: A data‐driven method survey

“…As shown in Table 6 , three investigations on prostate cancer have been reported for predicting pareto optimal dose distributions. 137 , 138 , 147 For each patient in the training set, 10, 100, and 500 plans were generated by Ma et al Nguyen et al and Bohara et al respectively, to sample the pareto surface with different tradeoffs. An optimal number of plans per patient in training set is unknown as it may depend on case to case basis.…”

“…To address this clinical tradeoff problem across different dose distributions, Nguyen et al proposed the differentiable loss function based on the DVH and adversarial loss in addition to traditional voxel-wise mean square error (MSE) loss to train the network. 147 Along the same line of work, Bohara et al incorporated beam information to predict pareto dose distribution using anatomy beam model proposed by Barragán-Montero et al 138 U-Net architecture has also been used for radiopharmaceutical dosimetry. 134,140 The network was trained to predict 3D dose rate maps given the mass density distribution and radioactivity maps.…”

Artificial intelligence applications in intensity modulated radiation treatment planning: an overview

“…While it is possible to estimate dose distribution using algorithms such as deformable image registration (50), most recent dose prediction algorithms are based on ML or DL models (51). Categorized by prediction algorithms, dose distribution could be predicted by shallow ML models (49,51), deep neural network models such as the convolutional neural network (CNN) (47,(52)(53)(54)(55)(56)(57)(58)(59) and the generative adversarial network (GAN) (48,60,61). Categorized by input/output dimensions, dose distribution could be predicted voxel by voxel (49,51), slice by slice (47,48,52,56,59,62), or as a 3D volume (53,54,57,58,60,61).…”

A feasibility study on deep learning‐based individualized 3D dose distribution prediction

“…Barragán‐Montero et al 9 extended this work to develop a more general model that considers variable beam configurations in addition to patient anatomy to predict dose distributions for lung intensity‐modulated radiation therapy (IMRT), thus indicating a potentially easier clinical implementation with no need to train specific models for different beam settings. Similar ideas have been implemented by different research groups and applied to various clinical scenarios 10−13 …”