PURPOSE: Few features characterizing the dosimetric properties of the patients are included in currently available dose-volume histogram (DVH) prediction models, making it intractable to build a correlative relationship between the input and output parameters. Here, we use planning target volume (PTV)-only treatment plans of the patients (i.e., the achievable dose distribution in the absence of organs-at-risk (OAR) constraints) to estimate the potentially achievable quality of treatment plans and establish a machine learning-based DVH prediction framework with the use of the dosimetric metric as model input parameters. METHODS: A support vector regression (SVR) approach was used as the backbone of our machine learning model. A database containing volumetric modulated arc therapy (VMAT) plans of 63 prostate cancer patients were used. For each patient, the PTV-only plan was generated first. A correlative relationship between the OAR DVH of the PTV-only plan (model input) and the corresponding DVH of the clinical treatment plan (CTP) (model output) was then established with the 53 training cases. The prediction model was tested by the validation cohort of ten cases. RESULTS: For the training cohort, the checks of dosimetric endpoints (DEs) indicated that 52 of 53 plans (98%) were within the 10% error bound for bladder, and 45 of 53 plans (85%) were within the 10% error bound for rectum. In the validation tests, 92% and 96% of the DEs were within the 10% error bounds for bladder and rectum, respectively, and eight of ten validation plans (80%) were within the 10% error bound for both the bladder and rectum. The sum of absolute residuals (SAR) achieved a mean of 0.034 ± 0.028 and 0.046 ± 0.021 for the bladder and rectum, respectively. CONCLUSIONS: A novel dosimetric features-driven machine learning model with the use of PTV-only plan has been established for DVH prediction. The framework is capable of efficiently generating best achievable DVHs for VMAT planning.
PURPOSE: Few features characterizing the dosimetric properties of the patients are included in currently available dose-volume histogram (DVH) prediction models, making it intractable to build a correlative relationship between the input and output parameters. Here, we use planning target volume (PTV)-only treatment plans of the patients (i.e., the achievable dose distribution in the absence of organs-at-risk (OAR) constraints) to estimate the potentially achievable quality of treatment plans and establish a machine learning-based DVH prediction framework with the use of the dosimetric metric as model input parameters. METHODS: A support vector regression (SVR) approach was used as the backbone of our machine learning model. A database containing volumetric modulated arc therapy (VMAT) plans of 63 prostate cancerpatients were used. For each patient, the PTV-only plan was generated first. A correlative relationship between the OAR DVH of the PTV-only plan (model input) and the corresponding DVH of the clinical treatment plan (CTP) (model output) was then established with the 53 training cases. The prediction model was tested by the validation cohort of ten cases. RESULTS: For the training cohort, the checks of dosimetric endpoints (DEs) indicated that 52 of 53 plans (98%) were within the 10% error bound for bladder, and 45 of 53 plans (85%) were within the 10% error bound for rectum. In the validation tests, 92% and 96% of the DEs were within the 10% error bounds for bladder and rectum, respectively, and eight of ten validation plans (80%) were within the 10% error bound for both the bladder and rectum. The sum of absolute residuals (SAR) achieved a mean of 0.034 ± 0.028 and 0.046 ± 0.021 for the bladder and rectum, respectively. CONCLUSIONS: A novel dosimetric features-driven machine learning model with the use of PTV-only plan has been established for DVH prediction. The framework is capable of efficiently generating best achievable DVHs for VMAT planning.
Authors: Dennis van de Sande; Marjan Sharabiani; Hanneke Bluemink; Esther Kneepkens; Nienke Bakx; Els Hagelaar; Maurice van der Sangen; Jacqueline Theuws; Coen Hurkmans Journal: Phys Imaging Radiat Oncol Date: 2021-12-01