Knowledge-based planning, multicriteria optimization, and plan scorecards: A winning combination

Abstract

Background and purpose

The ESTRO 2023 Physics Workshop hosted the Fully-Automated Radiotherapy Treatment Planning (Auto-RTP) Challenge, where participants were provided with CT images from 16 prostate cancer patients (6 prostate only, 6 prostate + nodes, and 4 prostate bed + nodes) across 3 challenge phases with the goal of automatically generating treatment plans with minimal user intervention. Here, we present our team’s winning approach developed to swiftly adapt to both different contouring guidelines and treatment prescriptions than those used in our clinic.

Materials and methods

Our planning pipeline comprises two main components: 1) auto-contouring and 2) auto-planning engines, both internally developed and activated via DICOM operations. The auto-contouring engine employs 3D U-Net models trained on a dataset of 600 prostate cancer patients for normal tissues, 253 cases for pelvic lymph node, and 32 cases for prostate bed. The auto-planning engine, utilizing the Eclipse Scripting Application Programming Interface, automates target volume definition, field geometry, planning parameters, optimization, and dose calculation. RapidPlan models, combined with multicriteria optimization and scorecards defined on challenge scoring criteria, were employed to ensure plans met challenge objectives. We report leaderboard scores (0–100, where 100 is a perfect score) which combine organ-at-risk and target dose-metrics on the provided cases.

Results

Our team secured 1st place across all three challenge phases, achieving leaderboard scores of 79.9, 77.3, and 78.5 outperforming 2nd place scores by margins of 6.4, 0.4, and 2.9 points for each phase, respectively. Highest plan scores were for prostate only cases, with an average score exceeding 90. Upon challenge completion, a “Plan Only” phase was opened where organizers provided contours for planning. Our current score of 90.0 places us at the top of the “Plan Only” leaderboard.

Conclusions

Our automated pipeline demonstrates adaptability to diverse guidelines, indicating progress towards fully automated radiotherapy planning. Future studies are needed to assess the clinical acceptability and integration of automatically generated plans.