An automated workflow to improve efficiency in radiation therapy treatment planning by prioritizing organs-at-risk

Abstract Purpose Manual delineation (MD) of organs-at-risk (OAR) is time and labor-intensive. Autodelineation (AD) can reduce the need for MD, but, because current algorithms are imperfect, manual review and modification is still typically utilized. Recognizing that many OARs are sufficiently far from important dose levels that they do not pose a realistic risk, we hypothesize that some OARs can be excluded from MD and manual review with no clinical impact. The purpose of this study was to develop a method that automatically identifies these OARs and enables more efficient workflows that incorporate AD without degrading clinical quality. Methods and Materials Preliminary dose map estimates were generated for N=10 head-and-neck patients using only prescription and target-volume information. Conservative estimates of clinical OAR objectives were computed using AD structures with spatial expansion buffers to account for potential delineation uncertainties. OARs with estimated dose metrics below clinical tolerances were deemed low-priority and excluded from MD and/or manual review. Final plans were then optimized using high-priority MD OARs and low-priority AD OARs and compared with reference plans generated using all MD OARs. Multiple different spatial buffers were used to accommodate different potential delineation uncertainties. Results 67 out of 201 total OARs were identified as low-priority using the proposed methodology, which permitted a 33% reduction in structures requiring manual delineation/review. Plans optimized using low-priority AD OARs without review or modification met all planning objectives that were met when all MD OARs were used, indicating clinical equivalence. Conclusion Prioritizing OARs using estimated dose distributions allowed a substantial reduction in required manual delineation and review without affecting clinically relevant dosimetry.