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Optimal Endobronchial Ultrasound Strain Elastography Assessment Strategy: An Explorative Study.

ABSTRACT: BACKGROUND:In lung cancer staging, mediastinal lymph nodes are currently aspirated using endobronchial ultrasound transbronchial needle aspiration (EBUS-TBNA) based on size and FDG-PET avidity. EBUS strain elastography (SE) is a new technique that may help predict the presence of malignancy. However, a standardized assessment strategy for EBUS-SE measurement is lacking. OBJECTIVES:The aim of this study was to determine the optimal assessment strategy for investigating the predictive value of EBUS-SE in mediastinal lymph nodes. METHODS:Two qualitative visual analogue scale strain scores and two semiquantitative strain elastography measurements (a strain histogram and strain ratio) were acquired in 120 lymph nodes of 63 patients with (suspected) lung cancer. The dataset was randomized into an 80% training dataset to determine cut-off values. Performance was consecutively tested on the remaining 20% and the overall dataset. RESULTS:The semiquantitative mean histogram scoring strategy with a cut-off value of 78 (range 0-255) showed the best and most reproducible performance in prediction of malignancy with 93% overall sensitivity, 75% specificity, 69% positive predictive value, 95% negative predictive value, and 82% accuracy. Combining the EBUS-SE mean histogram scoring outcome with PET-CT information increased the post-test probability of disease in relevant clinical scenarios, having a positive test likelihood ratio of 4.16 (95% CI 2.98-8.13) and a negative test likelihood ratio of 0.14 (95% CI 0.04-2.81) in suspicious lymph nodes based on FDG-PET or CT imaging. CONCLUSIONS:EBUS-SE can potentially help predict lymph node malignancy in patients with lung cancer. The best semiquantitative assessment method is the mean strain histogram technique.

SUBMITTER: Verhoeven RLJ

PROVIDER: S-EPMC6492606 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Optimal Endobronchial Ultrasound Strain Elastography Assessment Strategy: An Explorative Study.

Verhoeven Roel L J RLJ de Korte Chris L CL van der Heijden Erik H F M EHFM

Respiration; international review of thoracic diseases 20181214 4

<h4>Background</h4>In lung cancer staging, mediastinal lymph nodes are currently aspirated using endobronchial ultrasound transbronchial needle aspiration (EBUS-TBNA) based on size and FDG-PET avidity. EBUS strain elastography (SE) is a new technique that may help predict the presence of malignancy. However, a standardized assessment strategy for EBUS-SE measurement is lacking.<h4>Objectives</h4>The aim of this study was to determine the optimal assessment strategy for investigating the predicti ...[more]

PMID: 30554224

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Project description:BackgroundEndoscopic ultrasound (EBUS) strain elastography can diagnose intrathoracic benign and malignant lymph nodes (LNs) by reflecting the relative stiffness of tissues. Due to strong subjectivity, it is difficult to give full play to the diagnostic efficiency of strain elastography. This study aims to use machine learning to automatically select high-quality and stable representative images from EBUS strain elastography videos.MethodsLNs with qualified strain elastography videos from June 2019 to November 2019 were enrolled in the training and validation sets randomly at a quantity ratio of 3:1 to train an automatic image selection model using machine learning algorithm. The strain elastography videos in December 2019 were used as the test set, from which three representative images were selected for each LN by the model. Meanwhile, three experts and three trainees selected one representative image severally for each LN on the test set. Qualitative grading score and four quantitative methods were used to evaluate images above to assess the performance of the automatic image selection model.ResultsA total of 415 LNs were included in the training and validation sets and 91 LNs in the test set. Result of the qualitative grading score showed that there was no statistical difference between the three images selected by the machine learning model. Coefficient of variation (CV) values of the four quantitative methods in the machine learning group were all lower than the corresponding CV values in the expert and trainee groups, which demonstrated great stability of the machine learning model. Diagnostic performance analysis on the four quantitative methods showed that the diagnostic accuracies were range from 70.33% to 73.63% in the trainee group, 78.02% to 83.52% in the machine learning group, and 80.22% to 82.42% in the expert group. Moreover, there were no statistical differences in corresponding mean values of the four quantitative methods between the machine learning and expert groups (p >0.05).ConclusionThe automatic image selection model established in this study can help select stable and high-quality representative images from EBUS strain elastography videos, which has great potential in the diagnosis of intrathoracic LNs.

Project description:BackgroundDuring endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA), the sonographic findings of B-mode imaging, as well as endobronchial elastography, can be obtained noninvasively and used for the prediction of nodal metastasis.MethodsPatients with lung cancer or suspected lung cancer who underwent EBUS-TBNA were recorded prospectively and reviewed retrospectively. Both the B-mode sonographic and elastographic findings were independently evaluated for each lymph node. The sonographic features were classified according to previously published criteria. If oval shape, indistinct margins, homogenous echogenicity, and the absence of coagulation necrosis sign were all observed by B-mode imaging, then the lymph node was judged to be benign by sonographic imaging. In addition, if the stiffer area comprised more than 31% of the entire lymph node area, then the lymph node was judged to be malignant by elastography. We compared the results of these imaging-based predictions with the pathological diagnoses.ResultsThe prevalence of nodal metastasis was 78/228 (34.2%). B-mode sonography predicted 95.8% of benign lymph nodes, and elastography predicted 72.1% of malignant lymph nodes. By combining the two modalities, 59 of 71 (83.1%) lymph nodes judged as malignant by both analyses were pathologically proven to be malignant, and 101 of 105 (96.2%) lymph nodes judged as benign by both analyses were pathologically proven to be benign.ConclusionThe combination of elastography and sonographic findings showed good sensitivity and a high negative predictive value, which may facilitate selecting the most suspicious lymph nodes for biopsy.Key pointsSignificant findings of the study. The combination of endobronchial elastography and sonography resulted in a higher diagnostic yield than either modality alone for predicting benign and malignant lymph nodes in patients with lung cancer.What this study addsThe combination of endobronchial elastography and sonography will help clinicians identify the most suspicious lymph nodes for puncturing during EBUS-TBNA, which may improve the efficiency of EBUS-TBNA.

Project description:Obtaining accurate displacement estimates along both axial (parallel to the acoustic beam) and lateral (perpendicular to the beam) directions is an important task for several clinical applications such as shear strain imaging, modulus reconstruction and temperature imaging, where a full description of the two or three-dimensional (2D/3D) deformation field is required. In this study we propose an improved speckle tracking algorithm where axial and lateral motion estimations are simultaneously performed to enhance motion tracking accuracy. More specifically, using conventional ultrasound echo data, this algorithm first finds an iso-contour in the vicinity of the peak correlation between two segments of the pre- and post-deformation ultrasound radiofrequency echo data. The algorithm then attempts to find the center of the iso-contour of the correlation function that corresponds to the unknown (sub-sample) motion vector between these two segments of echo data. This algorithm has been tested using computer-simulated data, studies with a tissue-mimicking phantom, and in vivo breast lesion data. Computer simulation results show that the method improves the accuracy of both lateral and axial tracking. Such improvements are more significant when the deformation is small or along the lateral direction. Results from the tissue-mimicking phantom study are consistent with findings observed in computer simulations. Using in vivo breast lesion data we found that, compared to the 2D quadratic subsample displacement estimation methods, higher quality axial strain and shear strain images (e.g. 18.6% improvement in contrast-to-noise ratio for shear strain images) can be obtained for large deformations (up to 5% frame-to-frame and 15% local strains) in a multi-compression technique. Our initial results demonstrated that this conceptually and computationally simple method could improve the image quality of ultrasound-based strain elastography with current clinical equipment.

Dataset Information

Optimal Endobronchial Ultrasound Strain Elastography Assessment Strategy: An Explorative Study.

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Optimal Endobronchial Ultrasound Strain Elastography Assessment Strategy: An Explorative Study.

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