Evaluation of deep learning-based deliverable VMAT plan generated by prototype software for automated planning for prostate cancer patients

Abstract

This study aims to evaluate the dosimetric accuracy of a deep learning (DL)-based deliverable volumetric arc radiation therapy (VMAT) plan generated using DL-based automated planning assistant system (AIVOT, prototype version) for patients with prostate cancer. The VMAT data (cliDose) of 68 patients with prostate cancer treated with VMAT treatment (70–74 Gy/28–37 fr) at our hospital were used (n = 55 for training and n = 13 for testing). First, a HD-U-net-based 3D dose prediction model implemented in AIVOT was customized using the VMAT data. Thus, a predictive VMAT plan (preDose) comprising AIVOT that predicted the 3D doses was generated. Second, deliverable VMAT plans (deliDose) were created using AIVOT, the radiation treatment planning system Eclipse (version 15.6) and its vender-supplied objective functions. Finally, we compared these two estimated DL-based VMAT treatment plans—i.e. preDose and deliDose—with cliDose. The average absolute dose difference of all DVH parameters for the target tissue between cliDose and deliDose across all patients was 1.32 ± 1.35% (range: 0.04–6.21%), while that for all the organs at risks was 2.08 ± 2.79% (range: 0.00–15.4%). The deliDose was superior to the cliDose in all DVH parameters for bladder and rectum. The blinded plan scoring of deliDose and cliDose was 4.54 ± 0.50 and 5.0 ± 0.0, respectively (All plans scored ≥4 points, P = 0.03.) This study demonstrated that DL-based deliverable plan for prostate cancer achieved the clinically acceptable level. Thus, the AIVOT software exhibited a potential for automated planning with no intervention for patients with prostate cancer.

Predicting voxel-level dose distributions of single-isocenter volumetric modulated arc therapy treatment plan for multiple brain metastases

“…With the successful applications of deep learning models in predicting dose distribution for many primary tumor sites such as the lung ( 25 , 26 ), head-and-neck ( 23 , 28 , 33 , 34 ), and prostate ( 21 , 35 ), it is interesting to investigate this application for brain metastasis. In the study, a deep U-net architecture ( 30 ), previously successfully applied to predict dose distribution for head-and-neck cancer patients, is used as the base model in predicting the dose distribution of the VMAT plan for brain metastasis.…”

“…In addition, other types of networks, such as Resnet (27,29,30) and , are also used for dose prediction. So far, the deep U-net-like architecture and its variants with various types of residual or dense blocks become the mainstream structure for dose prediction (34)(35)(36)(37)(38).…”