Machine learning for dose-volume histogram based clinical decision-making support system in radiation therapy plans for brain tumors

• Main Authors: Pawel Siciarz, Salem Alfaifi, Eric Van Uytven, Shrinivas Rathod, Rashmi Koul, Boyd McCurdy
• Format: Article
• Language: English
• Published: Elsevier 2021-11-01
• Series: Clinical and Translational Radiation Oncology
• Online Access: http://www.sciencedirect.com/science/article/pii/S2405630821000793

Purpose: To create and investigate a novel, clinical decision-support system using machine learning (ML). Methods and Materials: The ML model was developed based on 79 radiotherapy plans of brain tumor patients that were prescribed a total dose of 60 Gy delivered with volumetric-modulated arc therapy (VMAT). Structures considered for analysis included planning target volume (PTV), brainstem, cochleae, and optic chiasm. The model aimed to classify the target variable that included class-0 corresponding to plans for which the PTV treatment planning objective was met and class-1 that was associated with plans for which the PTV objective was not met due to the priority trade-off to meet one or more organs-at-risk constraints. Several models were evaluated using double-nested cross-validation and an area-under-the-curve (AUC) metric, with the highest performing one selected for further investigation. The model predictions were explained with Shapely additive explanation (SHAP) interaction values. Results: The highest-performing model was Logistic Regression achieving an accuracy of 93.8 ± 4.1% and AUC of 0.98 ± 0.02 on the testing data. The SHAP analysis indicated that the ΔD99% metric for PTV had the greatest influence on the model predictions. The least important feature was ΔDMAX for the left and right cochleae. Conclusions: The trained model achieved satisfactory accuracy and can be used by medical physicists in a data-driven quality assurance program as well as by radiation oncologists to support their decision-making process in terms of treatment plan approval and potential plan modifications. Model explanation analysis showed that the model relies on clinically valid logic when making predictions.