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

Dejun Zhou,Nobutaka Mukumoto,Mitsuhiro Nakamura,Hiroaki Tanabe,Takashi Mizowaki,Masaki Kokubo,Yusuke Iizuka,Michio Yoshimura,Yukinori Matsuo

doi:10.1186/s13014-022-02012-7

Abstract

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.

Highlights

In 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
The percentages of 3D prediction positional error within 2 mm were 95.1%, 92.6% and 85.6% for the convolutional neural network (CNN), adaptive neuro-fuzzy inference system (ANFIS), and regression models, respectively. This indicates that the CNN model showed the best performance among the three models
The CNN model was initially built as a single reference model with the historical dataset, and patient-specific transfer learning was later conducted during the training period

Summary

Introduction

In 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. Zhou et al Radiation Oncology (2022) 17:42 perform radiation therapy for such targets, as it sufficiently covers the range of movement [2]. In this approach, along with the target, the surrounding normal tissue is irradiated at a high dose, which may have adverse consequences. With recent advances in radiotherapy systems, fourdimensional (4D) radiotherapy can be performed in clinical practice In this approach, breath-hold, respiratory gating, and real-time tumor tracking (RTTT) techniques can be adopted to reduce the effects of respiratory motion [3]. Through the RTTT, the beam position can be changed with the target position, thereby minimizing the occurrence of the adverse events caused by the motion of the target without burdening the patient’s breath or extending the treatment time [4]

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Results

Discussion

Conclusion