W. Zhang1, L. Yu2, J. Zhang2, B. Yang2, N. Liu2, T. Pang2, J. Qiu2, and Q. Chen3; 1Academy of medical sciences, Peking Union medical college ,Peking Union Medical College Hospital, Beijing, China, 2Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China, 3MedMind Technology Co, Ltd, Beijing, China
Purpose/Objective(s): Brachytherapy (BT) plays a crucial role in cervical cancer treatment. This study aimed to develop a 3D dose prediction model for cervical BT using Convolutional Neural Network (CNN). Materials/
Methods: In this study, we introduced a dose prediction model guided to generate dose distributions with explicit anatomical mask guidance. The model encompassed 224 clinical cases, including 190 for training-validation and 34 for testing. For performance evaluation, DVH metrics and 3D Gamma analysis were employed. The results were compared with those obtained using a 3D U-net model. Results: DVH metrics for the test set, including HRCTV D90, HRCTV D95, HRCTV D100, bladder D2CC, sigmoid D2CC, rectum D2CC, and intestine D2CC, yielded values of 5.44±0.91, 5.05±0.88, 3.34±0.79, 4.39±1.53, 3.24±1.31, 3.03±1.87, and 2.71±1.79, respectively. The corresponding dose differences in these DVH metrics were 0.63±0.63, 0.60±0.61, 0.53±0.61, 1.21±0.85, 0.71±0.61, 1.16±1.09, and 0.86±0.58, respectively. The 3D gamma passing rates for the 3%/3mm criteria of HRCTV, bladder, sigmoid, rectum, and intestine were 0.95±0.04, 0.99±0.02, 1.00±0.02, 1.00±0.01, and 1.00±0.00, respectively. Conclusion: The 3D BT dose prediction system, based on a 3D anatomical mask-guided deep learning network, could accurately generate 3D dose distributions, offering decision support for automatic clinical BT treatment planning.
