Correlation of Optical Surface Respiratory Motion Signal and Internal Lung and Liver Tumor Motion: A Retrospective Single-Center Observational Study

Wang, Guangyu; Song, Xinyu; Li, Guangjun; Duan, Lian; Li, Zhibin; Dai, Guyu; Bai, Long; Xiao, Qing; Zhang, Xiangbin; Song, Yang; Bai, Song

doi:10.1177/15330338221112280

Cited by 6 publications

(4 citation statements)

References 56 publications

(79 reference statements)

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“…In addition, the targets in our study were the magnitudes of respiratory motion, rather than internal targets that were treated by a medical linear accelerators or surgical robots. However, several studies (Lee et al 2018, Park et al 2018, Wang et al 2022 have demonstrated the strength and stability of the correlation between internal targets and respiratory motion, which means that EEG neural signals-based respiratory motion tracking is a potential solution to respiratory motion of internal targets in radiotherapy and robotic surgery. More importantly, a simplified 256-channel EEG might be able to capture respiratory-related neural signals owing to that the 256-channel EEG was initially designed to capture various brain activities.…”

Section: Discussionmentioning

confidence: 99%

Neural signals-based respiratory motion tracking: a proof-of-concept study

Zhang,

Liu,

et al. 2023

Phys. Med. Biol.

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Objective.Respiratory motion tracking techniques can provide optimal treatment accuracy for thoracoabdominal radiotherapy and robotic surgery. However, conventional imaging-based respiratory motion tracking techniques are time-lagged owing to the system latency of medical linear accelerators and surgical robots. This study aims to investigate the precursor time of respiratory related neural signals and analyze the potential of neural signals-based respiratory motion tracking. Approach.The neural signals and respiratory motion from eighteen healthy volunteers were acquired simultaneously using a 256-channel scalp electroencephalography (EEG) system. The neural signals were preprocessed using the MNE python package to extract respiratory-related EEG neural signals. Cross-correlation analysis was performed to assess the precursor time and cross-correlation coefficient between respiratory-related EEG neural signals and respiratory motion.Main results.Respiratory-related neural signals that precede the emergence of respiratory motion are detectable via non-invasive EEG. On average, the precursor time of respiratory-related EEG neural signals was 0.68 second. The representative cross-correlation coefficients between EEG neural signals and respiratory motion of the eighteen healthy subjects varied from 0.22 to 0.87. Significance. Our findings suggest that neural signals have the potential to compensate for the system latency of medical linear accelerators and surgical robots. This indicates that neural signals-based respiratory motion tracking is a promising solution to respiratory motion and could be radiotherapy and robotic surgery.

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Section: Discussionmentioning

confidence: 99%

Neural signals-based respiratory motion tracking: a proof-of-concept study

Zhang,

Liu,

et al. 2023

Phys. Med. Biol.

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“…Aside from the ROI selection, Jiateng Wang et al noted that the correlation between tumor and skin motion can vary greatly between tumors and patients, implying that, when planning lung and liver SBRT motion management, individual patient and tumor characteristics should always be considered [28].…”

Section: Introductionmentioning

confidence: 99%

Optimizing choice of skin surrogates for surface guided stereotactic body radiotherapy of lung lesions using four-dimensional computed tomography

Leipold,

Alerić,

Mlinarić

et al. 2024

Preprint

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Image-guided radiotherapy supported by surface guidance can help track lower lung lesion respiratory motion while reducing a patient’s exposure to ionizing radiation. However, it is not always clear how skin respiratory motion magnitudes and correlations with lung lesion respiratory motion vary between different skin regions of interest (ROI). Four-dimensional Computed Tomography (4DCT) images provide information on both skin and lung respiratory motion and are routinely acquired for the purpose of treatment planning in our institution. Analysis of 4DCT images for 57 patients treated in our institution has been conducted to provide information on respiratory motion magnitudes of nine skin ROIs of the torso, a tracking structure (TS) representing lower lung lobe lesion, as well as respiratory motion correlations between nine ROIs and TS. The effects of gender and adipose tissue volume and distribution on these correlations and magnitudes have been analyzed. Significant differences between ROIs in both respiratory motion magnitudes and correlations to TS have been detected. An overall negative correlation between ROI respiratory magnitudes and the adipose tissue has been detected for ROIs with rib cage support. A weak to moderate negative correlation between adipose tissue volume and ROI-to-TS respiratory correlations has been detected for upper thorax ROIs. Respiratory magnitudes in regions without rib support tended to be larger for men than for women, but no differences in ROI-to-TS correlation between sexes have been detected. Described findings should be considered when choosing skin surrogates for lower lung lesion motion management.

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“… 20 , 21 The external/internal correlation between external and internal respiratory motion has nothing to do with tumor clinical characteristics such as tumor location, pulmonary function, and size, as well as patient breathing pattern, and other information. 20 , 22 Other findings suggest that external respiratory motion waveforms do not always accurately correspond to the tumor motion in lung cancer patients, 23 and an external surrogate may not be sufficient to predict complex tumor motion. 24 , 25 This may lead to the target location being missing when an external surrogate is used to guide treatment in lung cancer patients.…”

Section: Introductionmentioning

confidence: 99%

“… 27 In our previous study, the correlation was calculated considering the Spearman correlation coefficient (SCC) and SCC after support vector regression fitting. 22 However, these methods all require significant additional data acquisition and processing. In the study, we report a novel approach to predict the correlation based on CT radiomic features through machine learning.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Radiomics Model for External and Internal Respiratory Motion Correlation Prediction in Lung Tumor

Zhang

Song

et al. 2022

Technol Cancer Res Treat

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Objectives: The complexity and specificity of lung tumor motion render it necessary to determine the external and internal correlation individually before applying indirect tumor tracking. However, the correlation cannot be determined from patient respiratory and tumor clinical characteristics before treatment. The purpose of this study is to present a machine learning model for an external/internal correlation prediction that is based on computed tomography (CT) radiomic features. Methods: 4-dimensional computed tomography (4DCT) images of 67 patients were collected retrospectively, and the external/internal correlation of lung tumor was calculated based on Spearman's rank correlation coefficient. Radiomic features were extracted from average intensity projection and the light gradient boosting machine (LightGBM)-based cross-validation (the recursive elimination method) was used for feature selection. The LightGBM framework forecasting models with classification thresholds 0.7, 0.8, and 0.9 are established using stratified 5-fold cross-validation. Model performance was assessed using receiver operating characteristics, sensitivity, and specificity. Results: There were 16, 18, and 13 features selected for models 0.7, 0.8, and 0.9, respectively. Texture features are of great importance in external/internal correlation prediction compared to other features in all models. The sensitivities of the predictions in models 0.7, 0.8, and 0.9 were 0.800 ± 0.126, 0.829 ± 0.140, and 0.864 ± 0.086, respectively. The specificities were 0.771 ± 0.114, 0.936 ± 0.0581, and 0.839 ± 0.101, whereas the area under the curve (AUC) was 0.837, 0.946, and 0.877, respectively. Conclusions: Our findings indicate that radiomics is an effective tool for respiratory motion correlation prediction, which can extract tumor motion characteristics. We proposed a machine learning framework for correlation prediction in the motion management strategy for lung tumor patients.

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Correlation of Optical Surface Respiratory Motion Signal and Internal Lung and Liver Tumor Motion: A Retrospective Single-Center Observational Study

Cited by 6 publications

References 56 publications

Neural signals-based respiratory motion tracking: a proof-of-concept study

Neural signals-based respiratory motion tracking: a proof-of-concept study

Optimizing choice of skin surrogates for surface guided stereotactic body radiotherapy of lung lesions using four-dimensional computed tomography

Machine Learning Radiomics Model for External and Internal Respiratory Motion Correlation Prediction in Lung Tumor

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