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

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

Blog Article

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 luau thank you cards 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 acupatch 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.