Real-Time Respiratory Tumor Motion Prediction Based on a Temporal Convolutional Neural Network: Prediction Model Development Study

作者全名："Chang, Panchun; Dang, Jun; Dai, Jianrong; Sun, Wenzheng"

作者地址："[Chang, Panchun; Dang, Jun] Chongqing Med Univ, Affiliated Hosp 1, Dept Oncol, Chongqing, Peoples R China; [Chang, Panchun] Shandong Normal Univ, Sch Phys & Elect, Jinan, Peoples R China; [Dai, Jianrong] Chinese Acad Med Sci & Peking Union Med Coll, Canc Hosp, Natl Clin Res Ctr Canc, Dept Radiat Oncol,Natl Canc Ctr, Beijing, Peoples R China; [Sun, Wenzheng] Zhejiang Univ, Affiliated Hosp 2, Sch Med, Dept Radiat Oncol, 88 Jiefang Rd, Hangzhou 310009, Peoples R China"

通信作者："Sun, WZ (corresponding author), Zhejiang Univ, Affiliated Hosp 2, Sch Med, Dept Radiat Oncol, 88 Jiefang Rd, Hangzhou 310009, Peoples R China."

来源：JOURNAL OF MEDICAL INTERNET RESEARCH

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:000689731600008

JCR分区：Q1

影响因子：7.4

年份：2021

卷号：23

期号：8

开始页：　

结束页：　

文献类型：Article

关键词：radiation therapy; temporal convolutional neural network; respiratory signal prediction; neural network; deep learning model; dynamic tracking

摘要："Background: The dynamic tracking of tumors with radiation beams in radiation therapy requires the prediction of real-time target locations prior to beam delivery, as treatment involving radiation beams and gating tracking results in time latency. Objective: In this study, a deep learning model that was based on a temporal convolutional neural network was developed to predict internal target locations by using multiple external markers. Methods: Respiratory signals from 69 treatment fractions of 21 patients with cancer who were treated with the CyberKnife Synchrony device (Accuray Incorporated) were used to train and test the model. The reported model's performance was evaluated by comparing the model to a long short-term memory model in terms of the root mean square errors (RMSEs) of real and predicted respiratory signals. The effect of the number of external markers was also investigated. Results: The average RMSEs of predicted (ahead time=400 ms) respiratory motion in the superior-inferior, anterior-posterior, and left-right directions and in 3D space were 0.49 mm, 0.28 mm, 0.25 mm, and 0.67 mm, respectively. Conclusions: The experiment results demonstrated that the temporal convolutional neural network-based respiratory prediction model could predict respiratory signals with submillimeter accuracy."

基金机构：National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [62103366]; General Project of Chongqing Natural Science Foundation [cstc2020jcyj-msxm2928]; Seed Grant of the First Affiliated Hospital of Chongqing Medical University [PYJJ2019-208]; Chongqing Municipal Bureau of Human Resources and Social Security Fund [cx2018147]; Medical Research Key Project of Jiangsu Health Commission [ZDB 2020022]

基金资助正文："This work was partially supported by the National Natural Science Foundation of China (62103366), the General Project of Chongqing Natural Science Foundation (grant cstc2020jcyj-msxm2928), Seed Grant of the First Affiliated Hospital of Chongqing Medical University (grant PYJJ2019-208), Chongqing Municipal Bureau of Human Resources and Social Security Fund (grant cx2018147), and Medical Research Key Project of Jiangsu Health Commission (grant ZDB 2020022)."