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

Zhou, Dejun; Nakamura, Mitsuhiro; Mukumoto, Nobutaka; Tanabe, Hiroaki; Iizuka, Yusuke; Yoshimura, Masataka; Kitajima, Masaki; Matsuo, Yukinori

doi:10.1186/s13014-022-02012-7

Cited by 5 publications

(1 citation statement)

References 32 publications

(53 reference statements)

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“…Zhou Dejun et al observed that the regression-based prediction model does not represent the tumor motion accurately. CNN-driven prediction models were found to outperform the regression-based prediction model [29]. This paper proposes a Kinect v2 3D camera scheme driven by time-series deep-learning algorithmic models that can improve the accuracy of real-time tumor motion prediction compared with the regression model.…”

Section: Classification and Prediction Performance For Pseudopatient-...mentioning

confidence: 99%

3D Kinect Camera Scheme with Time-Series Deep-Learning Algorithms for Classification and Prediction of Lung Tumor Motility

Puangragsa

Setakornnukul

Dankulchai

et al. 2022

Sensors

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This paper proposes a time-series deep-learning 3D Kinect camera scheme to classify the respiratory phases with a lung tumor and predict the lung tumor displacement. Specifically, the proposed scheme is driven by two time-series deep-learning algorithmic models: the respiratory-phase classification model and the regression-based prediction model. To assess the performance of the proposed scheme, the classification and prediction models were tested with four categories of datasets: patient-based datasets with regular and irregular breathing patterns; and pseudopatient-based datasets with regular and irregular breathing patterns. In this study, ‘pseudopatients’ refer to a dynamic thorax phantom with a lung tumor programmed with varying breathing patterns and breaths per minute. The total accuracy of the respiratory-phase classification model was 100%, 100%, 100%, and 92.44% for the four dataset categories, with a corresponding mean squared error (MSE), mean absolute error (MAE), and coefficient of determination (R2) of 1.2–1.6%, 0.65–0.8%, and 0.97–0.98, respectively. The results demonstrate that the time-series deep-learning classification and regression-based prediction models can classify the respiratory phases and predict the lung tumor displacement with high accuracy. Essentially, the novelty of this research lies in the use of a low-cost 3D Kinect camera with time-series deep-learning algorithms in the medical field to efficiently classify the respiratory phase and predict the lung tumor displacement.

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Section: Classification and Prediction Performance For Pseudopatient-...mentioning

confidence: 99%

3D Kinect Camera Scheme with Time-Series Deep-Learning Algorithms for Classification and Prediction of Lung Tumor Motility

Puangragsa

Setakornnukul

Dankulchai

et al. 2022

Sensors

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Feasibility study of using next‐generation reservoir computing (NG‐RC) model to estimate liver tumor motion from external breathing signals

Samadi Miandoab,

Setayeshi,

Blanck

et al. 2024

Medical Physics

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BackgroundRespiratory motion is a challenge for accurate radiotherapy that may be mitigated by real‐time tracking. Commercial tracking systems utilize a hybrid external‐internal correlation model (ECM), integrating continuous external breathing monitoring with sparse X‐ray imaging of the internal tumor position.PurposeThis study investigates the feasibility of using the next generation reservoir computing (NG‐RC) model as a hybrid ECM to transform measured external motions into estimated 3D internal motions.MethodsThe NG‐RC model utilizes the nonlinear vector autoregressive (NVAR) machine to account for the hysteresis or phase differences between external and internal motions. The datasets used to evaluate the efficacy of the NG‐RC model include 57 motion traces from the CyberKnife system. The datasets were divided into three regions (central, lower, and upper livers) and three motion patterns. These patterns include linear and nonlinear motion patterns (Group A), hysteresis motion patterns (Group B), and all motion patterns (Group C). Moreover, various updating techniques were examined, such as continuously updating the NG‐RC model using the first‐in‐first‐out (FIFO) approach and sampling the internal tumor position every 0 s (strategy A), 60 s (strategy B), 30 s (strategy C), and 50 s (strategy D).ResultsThe NG‐RC model combined with strategy C resulted in better estimation accuracy than the reported CyberKnife cases (Wilcoxon signed rank p < 0.05). For linear and nonlinear motion patterns, the 3D radial estimation accuracy (mean ± SD) using the NG‐RC model combined with strategy C and the CyberKnife system was 1.20 ± 0.78 and 1.1 ± 0.20 mm in the central liver, 0.66 ± 0.25 and 1.49 ± 0.50 mm in the lower liver, and 1.73 ± 0.86 and 1.61 ± 0.42 mm in the upper liver. For hysteresis motion patterns, the corresponding values were 1.13 ± 0.37 and 1.45 ± 0.33 mm, 1.43 ± 1.30 and 1.67 ± 0.42 mm, and 1.20 ± 0.68 and 1.46 ± 0.54 mm in the central, lower, and upper livers, respectively.ConclusionThis study proposed a new hybrid correlation model for real‐time tumor tracking, which can be used to account for both linear and nonlinear motion patterns, as well as hysteresis motion patterns. Additionally, the NG‐RC model required shorter training data sets (15 s) during pre‐treatment and short internal motion sampling (every 30 s) during treatment compared to other ECMs.

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Quality and Safety Considerations for Adaptive Radiation Therapy: An ASTRO White Paper

Chetty,

Cai,

Chuong

et al. 2024

International Journal of Radiation Oncology*Biology*Physics

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

Cited by 5 publications

References 32 publications

3D Kinect Camera Scheme with Time-Series Deep-Learning Algorithms for Classification and Prediction of Lung Tumor Motility

3D Kinect Camera Scheme with Time-Series Deep-Learning Algorithms for Classification and Prediction of Lung Tumor Motility

Feasibility study of using next‐generation reservoir computing (NG‐RC) model to estimate liver tumor motion from external breathing signals

Quality and Safety Considerations for Adaptive Radiation Therapy: An ASTRO White Paper

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