Prospective study of artificial intelligence-based decision support to improve head and neck radiotherapy plan quality

BACKGROUND AND PURPOSE: Volumetric modulated arc therapy (VMAT) planning for head and neck cancer is a complex process. While the lowest achievable dose for each individual organ-at-risk (OAR) is unknown a priori, artificial intelligence (AI) holds promise as a tool to accurately estimate the expected dose distribution for OARs. We prospectively investigated the benefits of incorporating an AI-based decision support tool (DST) into the clinical workflow to improve OAR sparing. MATERIALS AND METHODS: The DST dose prediction model was based on 276 institutional VMAT plans. Under an IRB-approved prospective trial, the physician first generated a custom OAR directive for 50 consecutive patients (physician directive, PD). The DST then estimated OAR doses (AI directive, AD). For each OAR, the treating physician used the lower directive to form a hybrid directive (HD). The final plan metrics were compared to each directive. A dose difference of 3 Gray (Gy) was considered clinically significant. RESULTS: Compared to the AD and PD, the HD reduced OAR dose objectives by more than 3 Gy in 22% to 75% of cases, depending on OAR. The resulting clinical plan typically met these lower constraints and achieved mean dose reductions between 4.3 and 16 Gy over the PD, and 5.6 to 9.1 Gy over the AD alone. Dose metrics achieved using the HD were significantly better than institutional historical plans for most OARs and NRG constraints for all OARs. CONCLUSIONS: The DST facilitated a significantly improved treatment directive across all OARs for this generalized H&N patient cohort, with neither the AD nor PD alone sufficient to optimally direct planning.