A feasibility study of intrafractional tumor motion estimation based on 4D‐<scp>CBCT</scp> using diaphragm as surrogate

Zhou, Dingyi; Hong, Quan; Yan, Di; Chen, Shupeng; Qin, An; Stanhope, Carl; Lachaı̂ne, M.; Liang, Jian

doi:10.1002/acm2.12410

Cited by 9 publications

(6 citation statements)

References 24 publications

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“…We compared the motion between the diaphragm and the tumor by breathing phase matching and found that the maximum directional mean deviation was −1.8 mm. These results were similar to those of Zhou et al [ 24 ], indicating that the use of the diaphragm as a tracking surrogate in the CyberKnife tracking module provides similar performance to that of the movement of the tumor itself in the SI direction.…”

Section: Discussionsupporting

confidence: 89%

“…Previous studies have shown a good correlation between the diaphragm and liver tumors in SI and AP directions and indicated that the movement amplitude in the mediolateral direction was small and likely clinically irrelevant [ 16 , 17 ]. Based on the linear model, Cerviño et al [ 23 ] and Zhou et al [ 24 ] reported that the per-phase position, mean position, and excursion estimation errors were 1.12±0.99 mm, 0.97±0.88 mm, and 0.79±0.67 mm, respectively. Intrafractional per-phase tumor position estimation error, mean position error, and excursion error were within 3 mm, 95%, 96%, and 99% of the time, respectively.…”

Section: Discussionmentioning

confidence: 99%

“…Yang et al [ 16 ] found a good motion correlation between the diaphragm and liver tumor in superior-inferior (SI) and anterior-posterior (AP) directions using magnetic resonance imaging, suggesting that the diaphragm could be used as a reliable surrogate target for tracking liver tumors [ 17 – 20 ]. Previous studies have also shown a correlation between diaphragm motion and lung tumor motion by directly evaluating both using either 4-dimensional or fluoroscopic images [ 21 – 24 ].…”

Section: Introductionmentioning

confidence: 99%

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Using the Diaphragm as a Tracking Surrogate in CyberKnife Synchrony Treatment

Yang

Wang

et al. 2021

Med Sci Monit

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Background In this study, we assessed the usefulness of diaphragm surrogate tracking in the design of a respiratory model for CyberKnife Synchrony treatment of lung tumors. Material/Methods Twenty-four patients with lung cancer who underwent stereotactic body radiotherapy with CyberKnife between April and November 2019 were enrolled. Simulation plans for each patient were designed using Xsight lung tracking (XLT) and diaphragm tracking (DT) methods, and tumor visualization tests were performed. The offset consistency at each respiratory phase was analyzed. The relative distance along the alignment center of the superior-inferior (SI) axis in the 2 projections (dxAB), uncertainty (%), and average standard error (AvgStdErr)/maximum standard error (MAXStdErr) were also analyzed. Results Bland-Altman analyses revealed that the average differences±standard deviation (SD) between XLT and DT tracking methods were 0.4±2.9 mm, 0.3±4.35 mm, and -1.8±6.8 mm for the SI, left-right (LR), and anterior-posterior (AP) directions, respectively. These results indicated high consistency in the SI and LR directions and poor consistency in the AP direction. Uncertainty differed significantly between XLT and DT (22.813±5.721% vs 9.384±3.799%; t =−5.236; P =0.0008), but we found no significant differences in dxAB, AvgStdErr, or MAXStdErr. Conclusions In the majority of cases, motion tracking by XLT and DT was consistent and synchronized in the SI directions, but not in the LR and AP directions. With a boundary margin of 0.3±4.35 mm and 1.8±6.8 mm for the LR and AP directions, DT may contribute to better implementation of CyberKnife Synchrony treatment in patients with lung tumors near the diaphragm that cannot be seen in tumor visualization tests.

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Section: Discussionsupporting

confidence: 89%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Using the Diaphragm as a Tracking Surrogate in CyberKnife Synchrony Treatment

Yang

Wang

et al. 2021

Med Sci Monit

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“…In recent years, image processing techniques to detect tumor itself in kilovoltage [ 20 , 21 ] or megavoltage [ 22 , 23 ] images without the fiducial markers have been reported. It has also been reported that an anatomical feature such as the diaphragm could be used instead of the metal markers [ 24 , 25 ]. These kinds of techniques are considered to be useful for patients who cannot have markers inserted.…”

Section: Discussionmentioning

confidence: 99%

Prediction of target position from multiple fiducial markers by partial least squares regression in real-time tumor-tracking radiation therapy

Ukon

Arai

Sugiyama

et al. 2021

Journal of Radiation Research

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The purpose of this work is to show the usefulness of a prediction method of tumor location based on partial least squares regression (PLSR) using multiple fiducial markers. The trajectory data of respiratory motion of four internal fiducial markers inserted in lungs were used for the analysis. The position of one of the four markers was assumed to be the tumor position and was predicted by other three fiducial markers. Regression coefficients for prediction of the position of the tumor-assumed marker from the fiducial markers’ positions is derived by PLSR. The tracking error and the gating error were evaluated assuming two possible variations. First, the variation of the position definition of the tumor and the markers on treatment planning computed tomograhy (CT) images. Second, the intra-fractional anatomical variation which leads the distance change between the tumor and markers during the course of treatment. For comparison, rigid predictions and ordinally multiple linear regression (MLR) predictions were also evaluated. The tracking and gating errors of PLSR prediction were smaller than those of other prediction methods. Ninety-fifth percentile of tracking/gating error in all trials were 3.7/4.1 mm, respectively in PLSR prediction for superior–inferior direction. The results suggested that PLSR prediction was robust to variations, and clinically applicable accuracy could be achievable for targeting tumors.

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“…Fluoroscopic imaging in the linear accelerator is a conventional tool for position verification before radiotherapy. Some studies have reported using fluoroscopic imaging for direct tumor tracking with template matching applied as the localization algorithm (7)(8)(9)(10). However, the problem with tracking tumor motion in fluoroscopic images is that it is difficult to identify tumor targets because of the poor image contrast.…”

Section: Introductionmentioning

confidence: 99%

Automatic prediction model for online diaphragm motion tracking based on optical surface monitoring by machine learning

Dai¹,

He²,

Zhu³

et al. 2023

Quant Imaging Med Surg

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Background: The aim of this study was to establish a correlation model between external surface motion and internal diaphragm apex movement using machine learning and to realize online automatic prediction of the diaphragm motion trajectory based on optical surface monitoring. Methods:The optical body surface parameters and kilovoltage (kV) X-ray fluoroscopic images of 7 liver tumor patients were captured synchronously for 50 seconds. The location of the diaphragm apex was manually delineated by a radiation oncologist and automatically detected with a convolutional network model in fluoroscopic images. The correlation model between the body surface parameters and the diaphragm apex of each patient was developed through linear regression (LR) based on synchronous datasets before radiotherapy.Model 1 (M1) was trained with data from the first 30 seconds of the datasets and tested with data from the following 20 seconds of the datasets in the first fraction to evaluate the intra-fractional prediction accuracy.Model 2 (M2) was trained with data from the first 30 seconds of the datasets in the next fraction. The motion trajectory of the diaphragm apex during the following 20 seconds in the next fraction was predicted with M1 and M2, respectively, to evaluate the inter-fractional prediction accuracy. The prediction errors of the 2 models were compared to analyze whether the correlation model needed to be re-established. Results:The average mean absolute error (MAE) and root mean square error (RMSE) using M1 trained with automatic detection location for the first fraction were 3.12±0.80 and 3.82±0.98 mm in the superiorinferior (SI) direction and 1.38±0.24 and 1.74±0.32 mm in the anterior-posterior (AP) direction, respectively.The average MAE and RMSE of M1 versus M2 in the AP direction were 2.63±0.71 versus 1.28±0.48 mm and 3.26±0.90 versus 1.61±0.60 mm, respectively. The average MAE and RMSE of M1 versus M2 in the SI direction were 5.84±1.22 versus 3.37±0.43 mm and 7.22±1.45 versus 4.07±0.54 mm, respectively. The prediction accuracy of M2 was significantly higher than that of M1. Conclusions:This study shows that it is feasible to use optical body surface information to automatically predict the diaphragm motion trajectory. At the same time, it is necessary to establish a new correlation model for the current fraction before each treatment.

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A feasibility study of intrafractional tumor motion estimation based on 4D‐CBCT using diaphragm as surrogate

Cited by 9 publications

References 24 publications

Using the Diaphragm as a Tracking Surrogate in CyberKnife Synchrony Treatment

Using the Diaphragm as a Tracking Surrogate in CyberKnife Synchrony Treatment

Prediction of target position from multiple fiducial markers by partial least squares regression in real-time tumor-tracking radiation therapy

Automatic prediction model for online diaphragm motion tracking based on optical surface monitoring by machine learning

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