A Decision Support Software for AI-Assisted Decision Making in Response-Adaptive Radiotherapy — An Evaluation Study

Abstract

<h3>Purpose/Objective(s)</h3> To enable response-adaptive radiotherapy (RT) of optimized outcomes, a patient's treatment plan is adjusted according to their during-treatment dose response. However, this involves a multitude of variables making an unassisted decision-making process too challenging for physicians. To aid physicians in such a complex process, we have developed ARCliDS, a software tool for AI-assisted decision-making in adaptive RT. <h3>Materials/Methods</h3> ARCliDS was developed using Python as the backend and R Shiny as a frontend user interface. The underlying technology consists of two components: Artificial RT Environment (ARTE) and Deep Reinforcement Learning (DRL). ARTE was designed as a Markov Decision Process and modeled using supervised learning. Given a patient's pre- and mid-treatment information, ARTE can predict dose response and evaluate treatment outcomes for a selected dose fractionation value. DRL then utilizes the trained ARTE to search for the optimal mid-treatment dose adaptation for maximal tumor control probability and minimal normal tissue complication probability. <h3>Results</h3> We customized ARCliDS for non-small cell lung cancer (NSCLC) RT and trained it on a cohort of 67 NSCLC patients who had received adaptive RT and had local control and radiation induced pneumonitis endpoint available. We designed a set of questionnaires that consisted of an unassisted Blind phase and an AI-assisted Seen Phase, to evaluate physicians' trust level in AI recommendations. A radiation oncologist evaluated ARCliDS on 8 (∼10%) randomly chosen patients from the training cohort. The preliminary study shows that on average [Gy/frac] (stdev) the AI-assisted physician decision was 0.59 (0.78), 0.38 (1.44) and 0.7 (1.1) higher than the unassisted decision, AI recommendation, and retrospective clinical decision, respectively. The unassisted physician dose decision was only 0.1 (1.3) higher and the AI recommendation was 0.3 (1.2) higher than the clinical decision. Physician's subjective confidence level in their own decision was 6.9 (1.6), and their objective trust level in AI was 6.9 (3.1) out of 10. <h3>Conclusion</h3> The preliminary results showed that un-assisted decisions tend to be conservative. AI-assistance can help physicians in prescribing more optimal doses with higher confidence. The level of physician's trust in AI can be on par with the confidence in their own decision.