Automatic treatment planning for cervical cancer radiation therapy using direct three‐dimensional patient anatomy match

PURPOSE: Current knowledge-based planning methods for radiation therapy mainly use low-dimensional features extracted from contoured structures to identify geometrically similar patients. Here, we propose a knowledge-based treatment planning method where the anatomical similarity is quantified by the rigid registration of the three-dimensional (3D) planning target volume (PTV) and organs at risks (OARs) between an incoming patient and database patients. METHODS: A database that contains PTV and OARs contours from 81 cervical cancer radiation therapy patients was established. To identify the anatomically similar patients, the PTV of the new patient was registered to each PTV in the database and the Dice similarity coefficients were calculated for the PTV, rectum, and bladder between the new patient and database patients. Then the top 20 patients in the PTV match and top 3 patients in the subsequent bladder or rectum match were selected. The best dose-volume histogram parameters from the top three patients were applied as the dose constraints to the automatic plan optimization. A fast Fourier transform algorithm was developed to accelerate the 3D PTV registration process run through the database. The entire treatment planning process was automated using in-house customized Pinnacle scripts. The automatic plans were generated for 20 patients using leave-one-out scheme and were evaluated against the corresponding clinical plans. RESULTS: by 11.79% ± 5.2% (p < 0.01) and 2.85% ± 3.16% (p < 0.01), respectively. The dose parameters achieved for the PTV and other OARs were comparable to those in the clinical plans. The entire planning process, including both dose prediction and inverse optimization, costs about 6 min. CONCLUSIONS: The direct 3D contour match method utilizes the full spatial information of the PTV and OARs of interest and provides an intuitive measurement for patient plan anatomy similarity. The proposed automatic planning method can generate plans with better quality and higher efficiency.