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: The automatic plans significantly reduced rectum and bladder V 50 Gy ${V_{50\,\,{\rm{Gy}}}}$ 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.

Accelerate treatment planning process using deep learning generated fluence maps for cervical cancer radiation therapy.

PURPOSE: This study aims to develop a deep learning method that skips the time-consuming inverse optimization process for automatic generation of machine-deliverable intensity-modulated radiation therapy (IMRT) plans. METHODS: Ninety cervical cancer clinical IMRT plans were collected to train a two-stage convolution neural network, of which 66 plans were assigned for training, 11 for validation, and 13 for test. The neural network took patients' computed tomography (CT) anatomy as the input and predicted the fluence map for each radiation beam. The predicted fluence maps were then imported into a treatment planning system and converted to multileaf collimators motion sequences. The automatic plan was evaluated against its corresponding clinical plan, and its machine deliverability was validated by patient-specific IMRT quality assurance (QA). RESULTS: There were no significant differences in dose parameters between automatic and clinical plans for all 13 test patients, indicating a good prediction of fluence maps and a decent quality of automatic plans. The average dice similarity coefficient of isodose volumes encompassed by 0%-100% isodose lines ranged from 0.94 to 1. In patient-specific IMRT QA, the mean gamma passing rate of automatic plans achieved 99.5% under 3%/3 mm criteria, and 97.3% under 2%/2 mm criteria, with a low dose threshold of 10%. CONCLUSIONS: The proposed deep learning framework can produce machine-deliverable IMRT plans with quality similar to the clinical plans in the test set. It skips the inverse plan optimization process and provides an effective and efficient method to accelerate treatment planning process.