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

ABSTRACT

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.