RESUMO
Objective:To evaluate the practicability of dose volume histogram (DVH) prediction model for organ at risk (OAR) of radiotherapy plan by minimizing the cost function based on equivalent uniform dose (EUD).Methods:A total of 66 nasopharyngeal carcinoma (NPC) patients received volume rotational intensity modulated arc therapy (VMAT) at Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences from 2020 to 2021 were retrospectively selected for this study. Among them, 50 patients were used to train the recurrent neutral network (RNN) model and the remaining 16 cases were used to test the model. DVH prediction model was constructed based on RNN. A three-dimensional equal-weighted 9-field conformal plan was designed for each patient. For each OAR, the DVHs of individual fields were acquired as the model input, and the DVH of VMAT plan was regarded as the expected output. The prediction error obtained by minimizing EUD-based cost function was employed to train the model. The prediction accuracy was characterized by the mean and standard deviation between predicted and true values. The plan was re-optimized for the test cases based on the DVH prediction results, and the consistency and variability of the EUD and DVH parameters of interest (e.g., maximum dose for serial organs such as the spinal cord) were compared between the re-optimized plan and the original plan of OAR by the Wilcoxon paired test and box line plots.Results:The neural network obtained by training the cost function based on EUD was able to obtain better DVH prediction results. The new plan guided by the predicted DVH was in good agreement with the original plan: in most cases, the D 98% in the planning target volume (PTV) was greater than 95% of the prescribed dose for both plans, and there was no significant difference in the maximum dose and EUD in the brainstem, spinal cord and lens (all P>0.05). Compared with the original plan, the average reduction of optic chiasm, optic nerves and eyes in the new plans reached more than 1.56 Gy for the maximum doses and more than 1.22 Gy for EUD, and the average increment of temporal lobes reached 0.60 Gy for the maximum dose and 0.30 Gy for EUD. Conclusion:The EUD-based loss function improves the accuracy of DVH prediction, ensuring appropriate dose targets for treatment plan optimization and better consistency in the plan quality.
RESUMO
Objective:To establish a local effect model (LEM)-based rectal dose volume histogram (DVH) prediction model in prostate cancer patients treated with carbon ion therapy based on Japanese experience, aiming to provide reference for clinically reducing the incidence of rectal adverse reactions.Methods:The planning CT data of 76 patients with prostate cancer were collected. The microdosimetric kinetic model (MKM) was used for initial planning, and the LEM was selected to recalculate the biological dose based on the same fields to MKM. Then, the geometric features and DVH of the rectum were extracted from the LEM plans. The planning data of 61 cases were used to establish the prediction model with linear regression and the other 15 cases were used for validation.Results:The ratio of the overlapped volume between the rectum and the region of interest (ROI) expended from planning target volume by 1 cm along the left and right directions of the rectum could be proved to be the characteristic parameters for linear regression. The mean goodness-of-fit R2 of predicted and LEM plan-based DVH of 15 cases was 0.964. The results of predicted rectal adverse reactions based on predicted DVH were consistent with those of LEM plan-based DVH. Conclusions:The linear regression method used in this study can establish an accurate prediction model of rectal DVH, which may provide certain reference for reducing the incidence of rectal adverse reactions. Nevertheless, the findings remain to be further verified by clinical trials with larger sample size.