Development of AI-driven prediction models to realize real-time tumor tracking during radiotherapy

Radiation Oncology

Takashi Mizowaki¹  Yukinori Matsuo¹  Nobutaka Mukumoto¹  Yusuke Iizuka¹  Michio Yoshimura¹  Masaki Kokubo²  Hiroaki Tanabe³  Dejun Zhou⁴  Mitsuhiro Nakamura⁵ 

[1] Department of Radiation Oncology and Image-Applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, Japan;Department of Radiation Oncology, Kobe City Medical Center General Hospital, Hyogo, Japan;Department of Radiological Technology, Kobe City Medical Center General Hospital, Hyogo, Japan;Division of Medical Physics, Department of Information Technology and Medical Engineering, Human Health Sciences, Graduate School of Medicine, Kyoto University, 53 Kawahara-Cho, Shogoin, 606-8507, Sakyo-ku, Kyoto, Japan;Division of Medical Physics, Department of Information Technology and Medical Engineering, Human Health Sciences, Graduate School of Medicine, Kyoto University, 53 Kawahara-Cho, Shogoin, 606-8507, Sakyo-ku, Kyoto, Japan;Department of Radiation Oncology and Image-Applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, Japan;

关键词: Real-time tumor tracking;    Tumor motion prediction;    Convolutional neural network;    Adaptive neuro-fuzzy inference system;   

DOI  :  10.1186/s13014-022-02012-7

来源: Springer

【 摘 要 】

BackgroundIn infrared reflective (IR) marker-based hybrid real-time tumor tracking (RTTT), the internal target position is predicted with the positions of IR markers attached on the patient’s body surface using a prediction model. In this work, we developed two artificial intelligence (AI)-driven prediction models to improve RTTT radiotherapy, namely, a convolutional neural network (CNN) and an adaptive neuro-fuzzy inference system (ANFIS) model. The models aim to improve the accuracy in predicting three-dimensional tumor motion.MethodsFrom patients whose respiration-induced motion of the tumor, indicated by the fiducial markers, exceeded 8 mm, 1079 logfiles of IR marker-based hybrid RTTT (IR Tracking) with the gimbal-head radiotherapy system were acquired and randomly divided into two datasets. All the included patients were breathing freely with more than four external IR markers. The historical dataset for the CNN model contained 1003 logfiles, while the remaining 76 logfiles complemented the evaluation dataset. The logfiles recorded the external IR marker positions at a frequency of 60 Hz and fiducial markers as surrogates for the detected target positions every 80–640 ms for 20–40 s. For each logfile in the evaluation dataset, the prediction models were trained based on the data in the first three quarters of the recording period. In the last quarter, the performance of the patient-specific prediction models was tested and evaluated. The overall performance of the AI-driven prediction models was ranked by the percentage of predicted target position within 2 mm of the detected target position. Moreover, the performance of the AI-driven models was compared to a regression prediction model currently implemented in gimbal-head radiotherapy systems.ResultsThe percentage of the predicted target position within 2 mm of the detected target position was 95.1%, 92.6% and 85.6% for the CNN, ANFIS, and regression model, respectively. In the evaluation dataset, the CNN, ANFIS, and regression model performed best in 43, 28 and 5 logfiles, respectively.ConclusionsThe proposed AI-driven prediction models outperformed the regression prediction model, and the overall performance of the CNN model was slightly better than that of the ANFIS model on the evaluation dataset.

【 授权许可】

CC BY