Medical physics · 2025
Knowledge-based trade-off prediction for NSCLC treatment planning using multi-output regression.
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Kunkyab Tenzin, Lei Yang, Guo Hao, Tam James, Samstein Robert, Rosenzweig Kenneth, Chao Ming, Xia Junyi, Liu Tian, Zhang Jiahan
Аннотация
BACKGROUND: Knowledge-based planning (KBP) is a data-driven approach that utilizes the knowledge from previous high-quality treatment plans to predict dose-volume histogram (DVH) parameters for organs-at-risk (OARs) in new cases. Research has demonstrated that KBP enhances plan quality, minimizes inter-patient and inter-institution variability, and significantly boosts time efficiency. However, current state-of-the-art KBP approaches only generate one set of planning goals for one point on the Pareto optimal surface without considering potential planning trade-offs.
PURPOSE: The objective of this study is to develop a KBP trade-off prediction model that can effectively assist clinical decision-making during the treatment planning process for patients with locally advanced non-small cell lung cancer (NSCLC).
METHODS: We created 13 volumetric-modulated arc therapy (VMAT) plan variations for each patient in our dataset (n = 53), consisting of one balanced plan and 12 alternative plans with trade-off considerations. These trade-off plans incorporated three levels (0-2) of sparing priority for each OAR, including the esophagus, lungs, heart, and spinal cord. The first three principal components (PCs) of each OAR-specific DVH were used as target variables, while 26 anatomical features served as predictors. The patients were randomly divided into a training set (80%) and a test set (20%). A forward feature selection process identified the top five anatomical features, which were then used to train a random forest multi-output regression model to predict the first three DVH PCs for each of the 13 plan-OAR variations. We compared the performance of our trade-off prediction model with that of a balanced model trained on the balanced plan without any trade-off considerations. The evaluation metrics included root-mean-square error (RMSE) and mean absolute error (MAE) for key dose-volume metrics of the DVH curves.
RESULTS: The trade-off prediction model significantly outperformed the balanced model in terms of average RMSE (5.32 vs. 27.3) compared to the planned DVHs for all 13 treatment plans. The trade-off model also achieved a lower MAE for all the clinical dose-volume metrics, including spinal cord Dmax (12.5 vs. 15.5 Gy, p < 0.01), esophagus Dmax (1.7 vs. 2.7 Gy, p < 0.01), left lung V20Gy (7.8% vs. 27.5%, p < 0.01), right lung V20Gy (7.4% vs. 27.8%, p < 0.01), and heart V30Gy (10.8% vs. 20.9%, p < 0.01).
CONCLUSION: Our proposed KBP trade-off model reliably predicts plan tradeoff variations for the treatment of NSCLC patients. Incorporating this model into the pre-planning process may serve as a decision support tool for physicians and planners by providing feasible trade-off estimations, thereby improving the efficiency of the treatment planning workflow.
PURPOSE: The objective of this study is to develop a KBP trade-off prediction model that can effectively assist clinical decision-making during the treatment planning process for patients with locally advanced non-small cell lung cancer (NSCLC).
METHODS: We created 13 volumetric-modulated arc therapy (VMAT) plan variations for each patient in our dataset (n = 53), consisting of one balanced plan and 12 alternative plans with trade-off considerations. These trade-off plans incorporated three levels (0-2) of sparing priority for each OAR, including the esophagus, lungs, heart, and spinal cord. The first three principal components (PCs) of each OAR-specific DVH were used as target variables, while 26 anatomical features served as predictors. The patients were randomly divided into a training set (80%) and a test set (20%). A forward feature selection process identified the top five anatomical features, which were then used to train a random forest multi-output regression model to predict the first three DVH PCs for each of the 13 plan-OAR variations. We compared the performance of our trade-off prediction model with that of a balanced model trained on the balanced plan without any trade-off considerations. The evaluation metrics included root-mean-square error (RMSE) and mean absolute error (MAE) for key dose-volume metrics of the DVH curves.
RESULTS: The trade-off prediction model significantly outperformed the balanced model in terms of average RMSE (5.32 vs. 27.3) compared to the planned DVHs for all 13 treatment plans. The trade-off model also achieved a lower MAE for all the clinical dose-volume metrics, including spinal cord Dmax (12.5 vs. 15.5 Gy, p < 0.01), esophagus Dmax (1.7 vs. 2.7 Gy, p < 0.01), left lung V20Gy (7.8% vs. 27.5%, p < 0.01), right lung V20Gy (7.4% vs. 27.8%, p < 0.01), and heart V30Gy (10.8% vs. 20.9%, p < 0.01).
CONCLUSION: Our proposed KBP trade-off model reliably predicts plan tradeoff variations for the treatment of NSCLC patients. Incorporating this model into the pre-planning process may serve as a decision support tool for physicians and planners by providing feasible trade-off estimations, thereby improving the efficiency of the treatment planning workflow.