Endobronchial Ultrasound Images Research Articles

Computed tomography-based radial endobronchial ultrasound image simulation of peripheral pulmonary lesions using deep learning.

Radial endobronchial ultrasound (R-EBUS) plays an important role during transbronchial sampling of peripheral pulmonary lesions (PPLs). However, existing navigational bronchoscopy systems provide no guidance for R-EBUS. To guide intraoperative R-EBUS probe manipulation, we aimed to simulate R-EBUS images of PPLs from preoperative computed tomography (CT) data using deep learning. Preoperative CT and intraoperative ultrasound data of PPLs in 250 patients who underwent R-EBUS-guided transbronchial lung biopsy were retrospectively collected. Two-dimensional CT sections perpendicular to the biopsy path were transformed into ultrasonic reflection and transmission images using an ultrasound propagation model to obtain the initial simulated R-EBUS images. A cycle generative adversarial network was trained to improve the realism of initial simulated images. Objective and subjective indicators were used to evaluate the similarity between real and simulated images. Wasserstein distances showed that utilizing the cycle generative adversarial network significantly improved the similarity between real and simulated R-EBUS images. There was no statistically significant difference in the long axis, short axis, and area between real and simulated lesions (all P > 0.05). Based on the experts' evaluation, a median similarity score of ≥4 on a 5-point scale was obtained for lesion size, shape, margin, internal echoes, and overall similarity. Simulated R-EBUS images of PPLs generated by our method can closely mimic the corresponding real images, demonstrating the potential of our method to provide guidance for intraoperative R-EBUS probe manipulation.

Automatic Segmentation of Mediastinal Lymph Nodes and Blood Vessels in Endobronchial Ultrasound (EBUS) Images Using Deep Learning.

Endobronchial ultrasound (EBUS) is used in the minimally invasive sampling of thoracic lymph nodes. In lung cancer staging, the accurate assessment of mediastinal structures is essential but challenged by variations in anatomy, image quality, and operator-dependent image interpretation. This study aimed to automatically detect and segment mediastinal lymph nodes and blood vessels employing a novel U-Net architecture-based approach in EBUS images. A total of 1161 EBUS images from 40 patients were annotated. For training and validation, 882 images from 30 patients and 145 images from 5 patients were utilized. A separate set of 134 images was reserved for testing. For lymph node and blood vessel segmentation, the mean ± standard deviation (SD) values of the Dice similarity coefficient were 0.71 ± 0.35 and 0.76 ± 0.38, those of the precision were 0.69 ± 0.36 and 0.82 ± 0.22, those of the sensitivity were 0.71 ± 0.38 and 0.80 ± 0.25, those of the specificity were 0.98 ± 0.02 and 0.99 ± 0.01, and those of the F1 score were 0.85 ± 0.16 and 0.81 ± 0.21, respectively. The average processing and segmentation run-time per image was 55 ± 1 ms (mean ± SD). The new U-Net architecture-based approach (EBUS-AI) could automatically detect and segment mediastinal lymph nodes and blood vessels in EBUS images. The method performed well and was feasible and fast, enabling real-time automatic labeling.

Clinical utility of artificial intelligence–augmented endobronchial ultrasound elastography in lymph node staging for lung cancer

ObjectiveEndobronchial ultrasound elastography produces a color map of mediastinal lymph nodes, with the color blue (level 60) indicating stiffness. Our pilot study demonstrated that predominantly blue lymph nodes, with a stiffness area ratio greater than 0.496, are likely malignant. This large-scale study aims to validate this stiffness area ratio compared with pathology. MethodsThis is a single-center prospective clinical trial where B-mode ultrasound and endobronchial ultrasound elastography lymph node images were collected from patients undergoing endobronchial ultrasound transbronchial needle aspiration for suspected or diagnosed non–small cell lung cancer. Images were fed to a trained deep neural network algorithm (NeuralSeg), which segmented the lymph nodes, identified the percent of lymph node area above the color blue threshold of level 60, and assigned a malignant label to lymph nodes with a stiffness area ratio above 0.496. Diagnostic statistics and receiver operating characteristic analyses were conducted. NeuralSeg predictions were compared with pathology. ResultsB-mode ultrasound and endobronchial ultrasound elastography lymph node images (n = 210) were collected from 124 enrolled patients. Only lymph nodes with conclusive pathology results (n = 187) were analyzed. NeuralSeg was able to predict 98 of 143 true negatives and 34 of 44 true positives, resulting in an overall accuracy of 70.59% (95% CI, 63.50-77.01), sensitivity of 43.04% (95% CI, 31.94-54.67), specificity of 90.74% (95% CI, 83.63-95.47), positive predictive value of 77.27% (95% CI, 64.13-86.60), negative predictive value of 68.53% (95% CI, 64.05-72.70), and area under the curve of 0.820 (95% CI, 0.758-0.883). ConclusionsNeuralSeg was able to predict nodal malignancy based on endobronchial ultrasound elastography lymph node images with high area under the receiver operating characteristic curve and specificity. This technology should be refined further by testing its validity and applicability through a larger dataset in a multicenter trial.

TransEBUS: The interpretation of endobronchial ultrasound image using hybrid transformer for differentiating malignant and benign mediastinal lesions

The purpose of this study is to establish a deep learning automatic assistance diagnosis system for benign and malignant classification of mediastinal lesions in endobronchial ultrasound (EBUS) images. EBUS images are in the form of video and contain multiple imaging modes. Different imaging modes and different frames can reflect the different characteristics of lesions. Compared with previous studies, the proposed model can efficiently extract and integrate the spatiotemporal relationships between different modes and does not require manual selection of representative frames. In recent years, Vision Transformer has received much attention in the field of computer vision. Combined with convolutional neural networks, hybrid transformers can also perform well on small datasets. This study designed a novel deep learning architecture based on hybrid transformer called TransEBUS. By adding learnable parameters in the temporal dimension, TransEBUS was able to extract spatiotemporal features from insufficient data. In addition, we designed a two-stream module to integrate information from three different imaging modes of EBUS. Furthermore, we applied contrastive learning when training TransEBUS, enabling it to learn discriminative representation of benign and malignant mediastinal lesions. The results show that TransEBUS achieved a diagnostic accuracy of 82% and an area under the curve of 0.8812 in the test dataset, outperforming other methods. It also shows that several models can improve performance by incorporating two-stream module. Our proposed system has shown its potential to help physicians distinguishing benign and malignant mediastinal lesions, thereby ensuring the accuracy of EBUS examination.

A risk-scoring model based on endobronchial ultrasound multimodal imaging for predicting metastatic lymph nodes in lung cancer patients.

Endobronchial ultrasound (EBUS) imaging is a valuable tool for predicting lymph node (LN) metastasis in lung cancer patients. This study aimed to develop a risk-scoring model based on EBUS multimodal imaging (grayscale, Doppler mode, elastography) to predict LN metastasis in lung cancer patients. This retrospective study analyzed 350 metastatic LNs in 314 patients with lung cancer and 124 reactive LNs in 96 patients with nonspecific inflammation. The sonographic findings were compared with the final pathology results and clinical follow-up. Univariate and multivariate logistic regression analyses were performed to evaluate the independent risk factors of metastatic LNs. According to the β coefficients of corresponding indicators in logistic regression analysis, a risk-scoring model was established. Receiver operating characteristic curve was applied to evaluate the predictive capability of model. Multivariate analysis showed that short axis >10 mm, distinct margin, absence of central hilar structure, presence of necrosis, nonhilar vascularity, and elastography score 4 to 5 were independent predictors of metastatic LNs. Both short axis and margin were scored 1 point, and the rest of independent predictors were scored 2 points. The combination of 3 EBUS modes had the highest area under the receiver operating characteristic and accuracy of 0.884 (95% confidence interval, 0.846-0.922) and 87.55%, respectively. The risk stratification was as follows: 0 to 2 points, malignancy rate of 11.11%, low suspicion; 3 to 10 points, malignancy rate of 86.77%, high suspicion. The risk-scoring model based on EBUS multimodal imaging can effectively evaluate metastatic LNs in lung cancer patients to support clinical decision making.

Machine learning approaches in the interpretation of endobronchial ultrasound images: a comparative analysis.

This study explores the application of machine learning (ML) in analyzing endobronchial ultrasound (EBUS) images for the detection of lymph node (LN) malignancy, aiming to augment diagnostic accuracy and efficiency. We investigated whether ML could outperform conventional classification systems in identifying malignant involvement of LNs, based on eight established sonographic features. Retrospective data from two tertiary care hospital bronchoscopy units were utilized, encompassing healthcare reports of patients who had undergone EBUS between January 2017 and March 2023. The ML model was trained and tested using MATLAB, with 80% of the data allocated for training/validation, and 20% for testing. Performance was evaluated based on validation and testing accuracy, and receiver operating characteristic curves with comparing trained models and existing classification rules. The study analyzed 992 LNs, with 42.3% malignancy prevalence. Malignant LNs showed characteristic features such as larger size and distinct margins. The fine tuned models achieved testing accuracies of 95.9% and 96.4% for fine Gaussian SVM and KNN, respectively. Corresponding AUROC's were 0.955 and 0.963, outperforming other similar studies and conventional analyses. Fine tuned ML applications like SVM and KNN, can significantly enhance the analysis of EBUS images, improving diagnostic accuracy.

Radial Endobronchial Ultrasound-guided Transbronchial Cryobiopsy versus Forceps Biopsy for the Diagnosis of Solitary Pulmonary Nodules: A Prospective Randomised Trial.

Improvements in pulmonary diagnostic imaging and the development of lung cancer screening are increasing the prevalence of Solitary pulmonary nodules (SPNs). Fluoroscopically guided radial endobronchial ultrasound (EBUS) with transbronchial forceps biopsy (TB-FB) has been the conventional diagnostic method. Transbronchial cryobiopsy (TB-CB) is an alternative biopsy method. We sought to compare transbronchial cryobiopsy to transbronchial forceps biopsy for the diagnosis of SPNs. A prospective, single-centre, randomised controlled trial was conducted at the Royal Adelaide Hospital (RAH). Patients with SPNs were randomised to either 5 transbronchial forceps biopsies or one transbronchial cryobiopsy. Complete blinding of investigators and participants was not possible, as transbronchial cryobiopsy required general anaesthesia. The primary outcome was diagnostic yield with secondary outcomes of specimen size, diagnostic yield for subsets challenging to access with forceps and safety. The overall diagnostic yield for the 28 enrolled subjects was 76.8%(22/28). The diagnostic yield was 91.7% (11/12 patients) for transbronchial cryobiopsy and 68.8% (11/16 patients) for forceps biopsy (p=0.14). Median biopsy sizes were consistently larger for the cryobiopsy arm at 7.0mm compared to 2.5mm(p<0.0001). An eccentric EBUS image signalling the probe was adjacent to the nodule occurred in 4/28 cases, and TB-CB confirmed a diagnosis in 3/3 randomised to this arm. There were no major complications with either technique. Transbronchial cryobiopsy under the guidance of fluoroscopy and radial EBUS facilitates larger biopsy specimens without a significant increase in major complications. Further research is required to confirm the effect on diagnostic yield; however, our study supports a role for TB-CB in the diagnosis of SPNs and small, nodule-adjacent biopsies. Reference number of R20160213(HREC/16/RAH/37).

Deep learning with test-time augmentation for radial endobronchial ultrasound image differentiation: a multicentre verification study

PurposeDespite the importance of radial endobronchial ultrasound (rEBUS) in transbronchial biopsy, researchers have yet to apply artificial intelligence to the analysis of rEBUS images.Materials and methodsThis study developed a convolutional...

A valuable endobronchial ultrasound scoring system predicting malignant lymph nodes.

This study aims to determine the sonographic criteria of lymph nodes to predict malignancy with endobronchial ultrasound. A total of 1,987 lymph nodes of 967 patients (666 males, 301 females; mean age: 62.1±11.9 years; range, 21 to 90 years) between May 2016 and July 2020 were retrospectively analyzed. The endobronchial ultrasound images of lymph nodes were evaluated according to the following criteria: size (short axis >1 cm), shape (round or oval), margin (distinct or indistinct), coagulation necrosis sign (present or absent), central hilar structure (present or absent) and echogenicity (homogeneous or heterogeneous). A scoring system was developed for predicting malignancy. A total of 765 (38.5%) of the lymph nodes were malignant. In the univariate analysis, size >1 cm, round shape, distinct margin, absence of central hilar structure, presence of coagulation necrosis sign, and heterogeneity were significant predictors of malignancy (p<0.001 for all). In the multivariate analysis, the main independent predictors were heterogeneity and presence of coagulation necrosis sign (odds ratio=5.9, 95% confidence interval: 4.2-8.2 vs. odds ratio=3.1 95% confidence interval: 2.2-4.5, respectively). A cut-off value for endobronchial ultrasound score of ≥4 increased the malignancy risk 30 times with a sensitivity of 84.7%, and specificity of 84.5%. Our study results show that endobronchial ultrasound scoring system with six criteria has a high sensitivity and specificity for predicting malignant lymph nodes.

Endobronchial Ultrasound Image Simulation for Image-Guided Bronchoscopy.

Accurate disease diagnosis and staging are essential for patients suspected of having lung cancer. The state-of-the-art minimally invasive tools used by physicians to perform these operations are bronchoscopy, for navigating the lung airways, and endobronchial ultrasound (EBUS), for localizing suspect extraluminal cancer lesions. While new image-guided systems enable accurate bronchoscope navigation close to a lesion, no means exists for guiding the final EBUS localization of an extraluminal lesion. We propose an EBUS simulation method to assist with EBUS localization. The method draws on a patient's chest computed-tomography (CT) scan to model the ultrasound signal propagation through the tissue media. The method, which is suitable for simulating EBUS images for both radial-probe and convex-probe EBUS devices, entails three steps: 1) image preprocessing, which generates a 2D CT equivalent of the EBUS scan plane; 2) EBUS scan-line computation, which models ultrasound transmission to map the CT plane into a preliminary simulated EBUS image; and 3) image post-processing, which increases realism by introducing simulated EBUS imaging effects and artifacts. Results show that the method produces simulated EBUS images that strongly resemble images generated live by a real device and compares favorably to an existing ultrasound simulation method. It also produces images at a rate greater than real time (i.e., 53 frames/sec). We also demonstrate a successful integration of the method into an image-guided EBUS bronchoscopy system. The method is effective and practical for procedure planning/preview and follow-on live guidance of EBUS bronchoscopy.

Visualizing intratumoral injections in lung tumors by endobronchial ultrasound.

Real-time endobronchial ultrasound images are crucial for the accurate placement of the needle in peribronchial lung tumors and lymph nodes for diagnostic sampling. Beyond its role as a diagnostic tool, ultrasound-guided bronchoscopy can also aid the delivery of anti-cancer agents intratumorally, enabling diagnosis, staging, and treatment to occur within the same anesthesia, reducing the patient's burden. However, determining drug retention and distribution in situ remains challenging, albeit pivotal in assessing the success or failure of the therapeutic intervention. We hypothesized that ultrasound images acquired by the bronchoscope during the injection can provide qualitative and quantitative real-time information about drug transport. As a proof-of-concept, we retrospectively analyzed 13 videos of intratumoral cisplatin injections in advanced non-small cell lung cancers. We identified the injection and performed quantitative analysis through image processing and segmentation algorithms and mathematical models in 5 of them. We were able to infer the unlikeliness of a laminar flow through interstitial pores in favor of the emergence of tissue fractures. These data imply that the structural integrity of the tumor is a critical determinant of the ultimate distribution of an intratumorally delivered agent.

Nondiagnostic, radial-probe endobronchial ultrasound-guided biopsy for peripheral lung lesions: The added value of radiomics from ultrasound imaging for predicting malignancy.

This study investigated whether radiomic features extracted from radial-probe endobronchial ultrasound (radial EBUS) images can assist in decision-making for subsequent clinical management in cases with indeterminate pathologic results. A total of 494 patients who underwent radial EBUS biopsy for lung nodules between January 2017 and December 2018 were allocated to our training set. For the validation set, 229 patients with radial EBUS biopsy results from January 2019 to April 2020 were used. A multivariate logistic regression analysis was used for feature selection and prediction modeling. In the training set, 157 (67 benign and 90 malignant) of 212 patients pathologically diagnosed as indeterminate were analyzed. In the validation set, 213 patients were diagnosed as indeterminate, and 158 patients (63 benign and 95 malignant) were included in the analysis. The performance of the radiomics-added model, which considered satellite nodules, linear arc, shape, patency of vessels and bronchi, echogenicity, spiculation, C-reactive protein, and minimum histogram, was 0.929 for the training set and 0.877 for the validation set, whereas the performance of the model without radiomics was 0.910 and 0.891, respectively. Although the next diagnostic step for indeterminate lung biopsy results remains controversial, integrating various factors, including radiomic features from radial EBUS, might facilitate decision-making for subsequent clinical management.

Prediction of Nodal Metastasis in Lung Cancer Using Deep Learning of Endobronchial Ultrasound Images.

Simple SummaryEndobronchial ultrasound-guided transbronchial aspiration is a minimally invasive and highly accurate modality for the diagnosis of lymph node metastasis and is useful for pre-treatment biomarker test sampling in patients with lung cancer. Endobronchial ultrasound image analysis is useful for predicting nodal metastasis; however, it can only be used as a supplemental method to tissue sampling. In recent years, deep learning-based computer-aided diagnosis using artificial intelligence technology has been introduced in research and clinical medicine. This study investigated the feasibility of computer-aided diagnosis for the prediction of nodal metastasis in lung cancer using endobronchial ultrasound images. The outcome of this study may help improve diagnostic efficiency and reduce invasiveness of the procedure.Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is a valid modality for nodal lung cancer staging. The sonographic features of EBUS helps determine suspicious lymph nodes (LNs). To facilitate this use of this method, machine-learning-based computer-aided diagnosis (CAD) of medical imaging has been introduced in clinical practice. This study investigated the feasibility of CAD for the prediction of nodal metastasis in lung cancer using endobronchial ultrasound images. Image data of patients who underwent EBUS-TBNA were collected from a video clip. Xception was used as a convolutional neural network to predict the nodal metastasis of lung cancer. The prediction accuracy of nodal metastasis through deep learning (DL) was evaluated using both the five-fold cross-validation and hold-out methods. Eighty percent of the collected images were used in five-fold cross-validation, and all the images were used for the hold-out method. Ninety-one patients (166 LNs) were enrolled in this study. A total of 5255 and 6444 extracted images from the video clip were analyzed using the five-fold cross-validation and hold-out methods, respectively. The prediction of LN metastasis by CAD using EBUS images showed high diagnostic accuracy with high specificity. CAD during EBUS-TBNA may help improve the diagnostic efficiency and reduce invasiveness of the procedure.

Ventilatory Strategy to Prevent Atelectasis During Bronchoscopy Under General Anesthesia: A Multicenter Randomized Controlled Trial (Ventilatory Strategy to Prevent Atelectasis -VESPA- Trial)

Atelectasis negatively influences peripheral bronchoscopy, increasing CT scan-body divergence, obscuring targets, and creating false-positive radial-probe endobronchial ultrasound (RP-EBUS) images. Can a ventilatory strategy reduce the incidence of atelectasis during bronchoscopy under general anesthesia? Randomized controlled study (1:1) in which patients undergoing bronchoscopy were randomized to receive standard ventilation (laryngeal mask airway, 100%Fio2, zero positive end-expiratory pressure [PEEP]) vsa ventilatory strategy to prevent atelectasis (VESPA) with endotracheal intubation followed by a recruitment maneuver, Fio2 titration (< 100%), and PEEP of 8 to 10cm H2O. All patients underwent chest CT imaging and a survey for atelectasis with RP-EBUS bilaterally on bronchial segments 6, 9, and 10 after artificial airway insertion (time 1) and 20 to 30min later (time 2). Chest CT scans were reviewed by a blinded chest radiologist. RP-EBUS images were assessed by three independent, blinded readers. The primary end point was the proportion of patients with any atelectasis (either unilateral or bilateral) at time 2 according to chest CT scan findings. Seventy-six patients were analyzed, 38 in each group. The proportion of patients with any atelectasis according to chest CT scan at time 2 was 84.2%(95%CI, 72.6%-95.8%) in the control group and 28.9%(95%CI, 15.4%-45.9%) in the VESPA group (P< .0001). The proportion of patients with bilateral atelectasis at time 2 was 71.1%(95%CI, 56.6%-85.5%) in the control group and 7.9%(95%CI, 1.7%-21.4%) in the VESPA group (P< .0001). At time 2, 3.84 ± 1.67 (mean ± SD) bronchial segments in the control group vs1.21 ± 1.63 in the VESPA group were deemed atelectatic (P< .0001). No differences were found in the rate of complications. VESPA significantly reduced the incidence of atelectasis, was well tolerated, and showed a sustained effect over time despite bronchoscopic nodal staging maneuvers. VESPA should be considered for bronchoscopy when atelectasis is to be avoided. ClinicalTrials.gov; No.: NCT04311723; URL: www. gov.

Malignant thoracic lymph node classification with deep convolutional neural networks on real-time endobronchial ultrasound (EBUS) images.

BackgroundThoracic lymph node (LN) evaluation is essential for the accurate diagnosis of lung cancer and deciding the appropriate course of treatment. Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is considered a standard method for mediastinal nodal staging. This study aims to build a deep convolutional neural network (CNN) for the automatic classification of metastatic malignancies involving thoracic LN, using EBUS-TBNA.MethodsPatients who underwent EBUS-TBNAs to assess the presence of malignancy in mediastinal LNs during a ten-month period at Severance Hospital, Seoul, Republic of Korea, were included in the study. Corresponding LN ultrasound images, pathology reports, demographic data, and clinical history were collected and analyzed.ResultsA total of 2,394 endobronchial ultrasound (EBUS) images of 1,459 benign LNs from 193 patients, and 935 malignant LNs from 177 patients, were collected. We employed the visual geometry group (VGG)-16 network to classify malignant LNs using only traditional cross-entropy for classification loss. The sensitivity, specificity, and accuracy of predicting malignancy were 69.7%, 74.3%, and 72.0%, respectively, and the overall area under the curve (AUC) was 0.782. We applied the new loss function to train the network and, using the modified VGG-16, the AUC improved to a value of 0.8. The sensitivity, specificity, and accuracy improved to 72.7%, 79.0%, and 75.8%, respectively. In addition, the proposed network can process 63 images per second on a single mainstream graphics processing unit (GPU) device, making it suitable for real-time analysis of EBUS images.ConclusionsDeep CNNs can effectively classify malignant LNs from EBUS images. Selecting LNs that require biopsy using real-time EBUS image analysis with deep learning is expected to shorten the EBUS-TBNA procedure time, increase lung cancer nodal staging accuracy, and improve patient safety.

INCIDENTAL DIAGNOSIS OF PERICARDIAL EFFUSION ON ENDOBRONCHIAL ULTRASOUND

TOPIC: Procedures TYPE: Medical Student/Resident Case Reports INTRODUCTION: Endobronchial Ultrasound (EBUS) is a state-of-the-art diagnostic tool for diagnosing and staging lung cancer and can recognize several structures of the mediastinum; lymph nodes, tumors, vessels, pericardium, and the heart. We herein report a case of an incidental de-novo finding of pericardial effusion seen during the EBUS. CASE PRESENTATION: A 55 year old male with a history of tobacco abuse presented to the clinic with worsening dyspnea, fatigue, cough, and weight loss, and a contrast-enhanced CT scan of the chest showed right lower lobe lung mass with enlarged mediastinal and hilar nodes. Pulmonary service was consulted, and the patient underwent a successful EBUS bronchoscopy with a fine need aspiration of the enlarged lymph nodes. EBUS imaging from the sub-carinal region through the anterior wall of the left lower lobe bronchus showed pericardial effusion and, the pericardium was identified above the left atrium. The patient underwent an urgent echocardiogram confirming pericardial effusion and pericardiocentesis drained 1200 ml fluid later proved to be metastatic adenocarcinoma of lung origin. DISCUSSION: EBUS is increasingly used to evaluate mediastinal lesions, diagnose and staging malignancy. Pericardiocentesis under echocardiographicGuidance is considered the procedure of choice for the evacuation of pericardial effusions. Given the advantage of visualizing pericardium and heart during the EBUS, multiple reports have documented aspiration of pericardial effusion through a regular EBUS-TBNA needle. CONCLUSIONS: We here report a rare case of incidental pericardial effusion seen during the EBUS and highlight the possibility of detecting pericardial effusions early as several mediastinum structures: lymph nodes, tumors, vessels, pericardium, and the heart are visualized during the EBUS. Therefore, physicians must be aware of the alternative use of EBUS as a new diagnostic approach in the early detection of pericardial effusions, allowing for early initiation of therapy in an otherwise potentially life-threatening condition. REFERENCE #1: (EBUS) Application for Benign and Malignant Pericardial Effusion (PE) Aspiration: Transbronchial Pericardial Effusion Aspiration (TPEA) with a Regular EBUS Transbronchial (TBNA) Needle under Apneic Nasal Jet-Catheter Ventilation.” J Biomed 1 (2016): 9-25. REFERENCE #2: Sharma, Rahul K., Arjun Khanna, and Deepak Talwar. "Endobronchial Ultrasound: A New Technique of Pericardiocentesis in Posterior Loculated Pericardial Effusion.” Chest 150.5 (2016): e121-e123. REFERENCE #3: Gella, Vishwanath, Snigdha Ghana, and Upendra Srinivas. "Concurrent diagnostic pericardiocentesis and subcarinal mediastinal lymph node aspiration by EBUS TBNA.” (2015). DISCLOSURES: No relevant relationships by Venkatkiran Kanchustambham, source=Web Response No relevant relationships by Swetha Saladi, source=Web Response No relevant relationships by Vinita Vaidya, source=Web Response

Endobronchial Ultrasound Multimodal Imaging for the Diagnosis of Intrathoracic Lymph Nodes

Background: Endobronchial ultrasound (EBUS) imaging is valuable in diagnosing intrathoracic lymph nodes (LNs), but there has been little analysis of multimodal imaging. This study aimed to comprehensively compare the diagnostic performance of single and multimodal combinations of EBUS imaging in differentiating benign and malignant intrathoracic LNs. Methods: Subjects from July 2018 to June 2019 were consecutively enrolled in the model group and July 2019 to August 2019 in the validation group. Sonographic features of three EBUS modes were analysed in the model group for the identification of malignant LNs from benign LNs. The validation group was used to verify the diagnostic efficiency of single and multimodal diagnostic methods built in the model group. Results: 373 LNs (215 malignant and 158 benign) from 335 subjects and 138 LNs (79 malignant and 59 benign) from 116 subjects were analysed in the model and validation groups, respectively. For single mode, elastography had the best diagnostic value, followed by grayscale and Doppler. The corresponding accuracies in the validation group were 83.3%, 76.8%, and 71.0%, respectively. Grayscale with elastography had the best diagnostic efficiency of multimodal methods. When at least two of the three features (absence of central hilar structure, heterogeneity, and qualitative elastography score 4–5) were positive, the sensitivity, specificity, and accuracy in the validation group were 88.6%, 78.0%, and 84.1%, respectively. Conclusions: In both model and validation groups, elastography performed the best in single EBUS modes, as well as grayscale combined with elastography in multimodal imaging. Elastography alone or combined with grayscale are feasible to help predict intrathoracic benign and malignant LNs.

Deep learning with convex probe endobronchial ultrasound multimodal imaging: A validated tool for automated intrathoracic lymph nodes diagnosis.

Background and Objectives:Along with the rapid improvement of imaging technology, convex probe endobronchial ultrasound (CP-EBUS) sonographic features play an increasingly important role in the diagnosis of intrathoracic lymph nodes (LNs). Conventional qualitative and quantitative methods for EBUS multimodal imaging are time-consuming and rely heavily on the experience of endoscopists. With the development of deep-learning (DL) models, there is great promise in the diagnostic field of medical imaging.Materials and Methods:We developed DL models to retrospectively analyze CP-EBUS images of 294 LNs from 267 patients collected between July 2018 and May 2019. The DL models were trained on 245 LNs to differentiate benign and malignant LNs using both unimodal and multimodal CP-EBUS images and independently evaluated on the remaining 49 LNs to validate their diagnostic efficiency. The human comparator group consisting of three experts and three trainees reviewed the same test set as the DL models.Results:The multimodal DL framework achieves an accuracy of 88.57% (95% confidence interval [CI] [86.91%–90.24%]) and area under the curve (AUC) of 0.9547 (95% CI [0.9451–0.9643]) using the three modes of CP-EBUS imaging in comparison to the accuracy of 80.82% (95% CI [77.42%–84.21%]) and AUC of 0.8696 (95% CI [0.8369–0.9023]) by experts. Statistical comparison of their average receiver operating curves shows a statistically significant difference (P < 0.001). Moreover, the multimodal DL framework is more consistent than experts (kappa values 0.7605 vs. 0.5800).Conclusions:The DL models based on CP-EBUS imaging demonstrated an accurate automated tool for diagnosis of the intrathoracic LNs with higher diagnostic efficiency and consistency compared with experts.

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

BackgroundConvex 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.MethodsPatients 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.ResultsEndobronchial 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.ConclusionsThe 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.

Can artificial intelligence distinguish between malignant and benign mediastinal lymph nodes using sonographic features on EBUS images?

Aims This study aimed to develop a new intelligent diagnostic approach using an artificial neural network (ANN). Moreover, we investigated whether the learning-method-guided quantitative analysis approach adequately described mediastinal lymphadenopathies on endobronchial ultrasound (EBUS) images. Methods In total, 345 lymph nodes (LNs) from 345 EBUS images were used as source input datasets for the application group. The group consisted of 300 and 45 textural patterns as input and output variables, respectively. The input and output datasets were processed using MATLAB. All these datasets were utilized for the training and testing of the ANN. Results The best diagnostic accuracy was 82% of that obtained from the textural patterns of the LNs pattern (89% sensitivity, 72% specificity, and 78.2% area under the curve). The negative predictive values were 81% compared to the corresponding positive predictive values of 83%. Due to the application group’s pattern-based evaluation, the LN pattern was statistically significant (p = .002). Conclusions The proposed intelligent approach could be useful in making diagnoses. Further development is required to improve the diagnostic accuracy of the visual interpretation.