Endobronchial Ultrasound Elastography Helps Identify Fibrotic Lymph Nodes in Sarcoidosis

Minimally Invasive Sampling of Mediastinal Lesions

This narrative review examines the existing literature on minimally invasive image-guided sampling techniques of mediastinal lesions gathered from international databases (Medline, PubMed, Scopus, and Google Scholar). Original studies, systematic reviews with meta-analyses, randomized controlled trials, and case reports published between January 2009 and November 2023 were included. Four authors independently conducted the search to minimize bias, removed duplicates, and selected and evaluated the studies. The review focuses on the recent advancements in mediastinal sampling techniques, including EBUS-TBNA, EUS-FNA and FNB, IFB, and nodal cryobiopsy. The review highlights the advantages of an integrated approach using these techniques for diagnosing and staging mediastinal diseases, which, when used competently, significantly increase diagnostic yield and accuracy.

Endobronchial Ultrasound-Based Support Vector Machine Model for Differentiating between Benign and Malignant Mediastinal and Hilar Lymph Nodes

INTRODUCTION

The aim of the study was to establish an ultrasonographic radiomics machine learning model based on endobronchial ultrasound (EBUS) to assist in diagnosing benign and malignant mediastinal and hilar lymph nodes (LNs).

METHODS

The clinical and ultrasonographic image data of 197 patients were retrospectively analyzed. The radiomics features extracted by EBUS-based radiomics were analyzed by the least absolute shrinkage and selection operator. Then, we used a support vector machine (SVM) algorithm to establish an EBUS-based radiomics model. A total of 205 lesions were randomly divided into training (n = 143) and validation (n = 62) groups. The diagnostic efficiency was evaluated by receiver operating characteristic (ROC) curve analysis.

RESULTS

A total of 13 stable radiomics features with non-zero coefficients were selected. The SVM model exhibited promising performance in both groups. In the training group, the SVM model achieved an ROC area under the curve (AUC) of 0.892 (95% CI: 0.885-0.899), with an accuracy of 85.3%, sensitivity of 93.2%, and specificity of 79.8%. In the validation group, the SVM model had an ROC AUC of 0.906 (95% CI: 0.890-0.923), an accuracy of 74.2%, a sensitivity of 70.3%, and a specificity of 74.1%.

CONCLUSION

The EBUS-based radiomics model can be used to differentiate mediastinal and hilar benign and malignant LNs. The SVM model demonstrated excellent potential as a diagnostic tool in clinical practice.

[The role of endoscopy in exploration of the mediastinum, indications and results]

Since 2005, endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) has emerged as a standard pulmonological tool. The procedure is safe and well tolerated by patients, with minimal morbidity and almost no mortality. A previous review on the technique was published in 2012. However, over the last ten years, a number of new studies have been published on "benign" (sarcoidosis, tuberculosis…) as well as "malignant" diseases (lung cancer, metastases of extra-thoracic cancers, search for mutations and specific oncogenic markers…). These developments have led to expanded indications for EBUS-TBNA, with which it is indispensable to be familiar, in terms of "staging" as well as "diagnosis". In view of optimizing lymph node sampling, several publications have described and discussed EBUS exploration by means of newly available tools (biopsy forceps, larger needles…), and proposed interpretation of the images thereby produced. Given the ongoing evolution of linear EBUS, it seemed indispensable that information on this marvelous tool be updated. This review is aimed at summarizing the novel elements we have found the most important.

The diagnostic utility of ultrasound elastography to differentiate tuberculosis and sarcoidosis during endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA)

Introduction

Elastography is a non-invasive tool that may allow differentiation between benign and malignant lymph nodes during endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA). In tuberculosis (TB) endemic areas, clinicoradiological features of mediastinal TB and sarcoidosis often overlap, rendering an accurate diagnosis challenging. There is interest in the identification of modalities to aid in this differentiation. There are currently no published data on the utility of EBUS-elastography in differentiating between TB and sarcoidosis.

Methods

Subjects undergoing EBUS-TBNA were prospectively enrolled, and elastography features were observed. Subjects with definitive diagnosis of TB or sarcoidosis were enrolled. The elastography features recorded included the three-colour classification patterns and strain ratio.

Results

We enrolled 96 subjects with a definitive diagnosis (53 with TB and 43 with sarcoidosis). Of the 27 patients in whom the lymph nodes were classified as type 1 on endobronchial ultrasound elastography colour pattern, 17 had a diagnosis of TB (62.9%), while 10 were sarcoidosis (37%). For type 2 lymph nodes, 20/45 (44.4%) were TB and 25/45 (55.6%) were sarcoidosis. Type 3 lymph nodes were TB in 16/24 (66.7%) and sarcoidosis in 8/24 (33.3%). In classifying type 1 as 'sarcoidosis' and Type 3 as 'tubercular', the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy were 48.5%, 55.6%, 66.7%, 37%, and 0.51, respectively. The strain ratio (Median [IQR]) was 1.29 (0.37-5.98) in TB and 2.10 (0.83-4.52) in sarcoidosis group (P = 0.48).

Conclusion

Ultrasound elastographic lymph node characteristics have a poor diagnostic utility to differentiate between TB and sarcoidosis during EBUS-TBNA.

Role of EBUS-TBNA in Non-Neoplastic Mediastinal Lymphadenopathy: Review of Literature

Mediastinal lymphadenopathy is a condition in which one or more mediastinal lymph nodes are enlarged for malignant or benign causes, generally more than 10 mm. For a long time, the only way to approach the mediastinum was surgery, while in last decades endoscopic techniques gained their role in neoplastic diseases. At the present time, EBUS is the technique of choice for studying the mediastinum in the suspicion of cancer, while there are not strong indications in guidelines for the study of benign mediastinal lymphadenopathy. We reviewed the literature, looking for evidence of the role of EBUS in the diagnostics of non-neoplastic mediastinal lymphadenopathy, with special regard for granulomatous disease, both infectious and non-infectious. EBUS is a reliable alternative to surgery in non-neoplastic mediastinal lymphadenopathy, even if more evidence is needed for granulomatous diseases other than tuberculosis and sarcoidosis.

Diagnostic value of endobronchial ultrasound elastography combined with rapid onsite cytological evaluation in endobronchial ultrasound-guided transbronchial needle aspiration

Background

Endobronchial ultrasound (EBUS) elastography has been used in EBUS-guided transbronchial needle aspiration (EBUS-TBNA) to identify malignant lymph nodes based on tissue stiffness. Rapid onsite cytological evaluation (ROSE) has been widely utilized for onsite evaluation of sample adequacy and for guiding sampling during EBUS-TBNA. The aim of this study was to investigate the diagnostic value of combined EBUS elastography and ROSE in evaluating mediastinal and hilar lymph node status.

Methods

Retrospective chart review was performed from December 2018 to September 2020. Patient demographics, EBUS elastography scores, and ROSE, pathologic, and clinical outcome data were collected. The EBUS elastography scores were classified as follows: Type 1, predominantly nonblue; Type 2, partially blue and partially nonblue; and Type 3, predominantly blue. A receiver operating characteristic curve was used to compare the sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, and negative likelihood ratio for evaluation of malignant lymph nodes among the EBUS elastography, ROSE, and EBUS combined with ROSE groups.

Results

A total of 245 patients (345 lymph nodes) were included. The sensitivity and specificity of the EBUS elastography group for the diagnosis of malignant lymph nodes were 90.51% and 57.26%, respectively. The sensitivity and specificity in the ROSE group were 96.32% and 79.05%, respectively. The sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio of EBUS elastography combined with ROSE were 86.61%, 92.65%, 11.78, and 0.14, respectively, and the area under the curve was 0.942.

Conclusions

Combining EBUS elastography and ROSE significantly increased the diagnostic value of EBUS-TBNA in evaluating mediastinal and hilar lymph node status compared to each method alone.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12890-021-01748-4.

Automatic Image Selection Model Based on Machine Learning for Endobronchial Ultrasound Strain Elastography Videos

Background

Endoscopic 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.

Methods

LNs 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.

Results

A 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).

Conclusion

The 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.

Diagnostic value of endobronchial ultrasound image features: A specialized review

Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) technology is important in the diagnosis of intrathoracic benign and malignant lymph nodes (LNs). With the development of EBUS imaging technology, its role in noninvasive diagnosis, as a supplement to pathology diagnosis, has been given increasing attention in recent years. Many studies have explored qualitative and quantitative methods for the three EBUS modes, as well as a variety of multimodal analysis methods, to find the optimal method for the noninvasive diagnosis using EBUS for LNs. Here, we review and comment on the research methods and predictive diagnostic value, discuss the existing problems, and look ahead to the future application of EBUS imaging.

Scoring model of convex probe endobronchial ultrasound multimodal imaging in differentiating benign and malignant lung lesions

Background

Convex probe endobronchial ultrasound images can reflect the morphology, blood flow status and stiffness of the lesions. Endobronchial ultrasound multimodal imaging has great value for the diagnosis of intrathoracic lymph nodes. This study aimed to analyze the application of endobronchial ultrasound multimodal imaging on lung lesions.

Methods

Patients undergoing endobronchial ultrasound-guided transbronchial needle aspiration in Shanghai Chest Hospital from July 2018 to December 2019 were retrospectively enrolled. Nine grayscale features (long and short axes, margin, shape, lobulation sign, echogenicity, necrosis, liquefaction, calcification, and air-bronchogram), blood flow volume and elastography five-score method were analyzed to explore the best diagnostic method. The gold standard for diagnosing lesions depends on the histological and cytopathological findings of endobronchial ultrasound-guided transbronchial needle aspiration, transthoracic biopsy, resected sample of lesions, microbiological examination or clinical follow-up of at least 6 months.

Results

Endobronchial ultrasound multimodal imaging of 97 malignant lung lesions and 19 benign lung lesions from 116 patients were analyzed. There were statistically significant differences in distinct margin, presence of lobulation sign, presence of necrosis, and elastography grading score 4–5 between malignant and benign lung lesions, among which presence of lobulation sign and elastography grading score 4–5 were independent predictors. A diagnostic scoring model was then constructed based on the above four features, and when two or more features were present, the sensitivity, specificity, positive predictive value, negative predictive value and accuracy for malignant lung lesions prediction were 92.78%, 57.89%, 91.84%, 61.11% and 87.07%, respectively.

Conclusions

The combination of endobronchial ultrasound grayscale and elastography has potential value for malignant and benign lung lesions differentiation. The diagnostic scoring model established in this study needs further validation to guide the malignant and benign diagnosis of lung lesions.

Endobronchial ultrasound-guided transbronchial needle aspiration in sarcoidosis: Beyond the diagnostic yield

Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is the commonly used technique for pathological confirmation of clinically suspected sarcoidosis, mostly owing to its consistently high success rate in the detection of granulomas. However, other possible advantages, which are less appreciated and often poorly studied, may also contribute to the wider use of EBUS-TBNA in the future. These advantages include refinement of differential diagnoses through the study of lymph node characteristics during B-mode examination; reduction of complications associated with bronchoscopy, as well as improved triage of the specimen for ancillary studies with the use of rapid on-site evaluation; optimization of the quality of the sample through the selection of a target area for biopsy with minimal vascularity and absence of calcifications by using the colour Doppler and the B-mode; and prediction of the presence of extensive lymph node fibrosis by using the strain elastography module. Yet, limitations and possible clinical drawbacks should also be acknowledged. Indeed, due to the lack of specificity of the pathology findings in EBUS-derived samples, the diagnosis of sarcoidosis is one of the exclusion and should remain essentially clinical. The external validity of EBUS-TBNA results in sarcoidosis is questionable, as they mainly derive from studies in populations with a high disease prevalence. Finally, the risk exists that the low morbidity and high diagnostic yield of EBUS-TBNA may lead to its overuse in patients with clinical/radiological findings specific enough to secure a clinical diagnosis of sarcoidosis.