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Endobronchial ultrasound elastography strain ratio for mediastinal lymph node diagnosis.

ABSTRACT: BACKGROUND:Ultrasound elastography is an imaging procedure that can assess the biomechanical characteristics of different tissues. The aim of this study was to define the diagnostic value of the endobronchial ultrasound (EBUS) elastography strain ratio of mediastinal lymph nodes in patients with a suspicion of lung cancer. The diagnostic values of the strain ratios were compared with the EBUS brightness mode (B-mode) features of selected mediastinal lymph nodes and with their cytological diagnoses. PATIENTS AND METHODS:This prospective, single-centre study enrolled patients with an indication for biopsy and mediastinal staging after a non-invasive diagnostic workup of a lung tumour. EBUS with standard B-mode evaluation and elastography with strain ratio measurement were performed before endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA). RESULTS:Thirty-three patients with 80 suspicious mediastinal lymph nodes were included. Malignant infiltration was confirmed in 34 (42.5%) lymph nodes. The area under the receiver operating characteristic curve for the strain ratio was 0.87 (p < 0.0001). At a strain ratio ? 8, the accuracy for malignancy prediction was 86.25% (sensitivity 88.24%, specificity 84.78%, positive predictive value [PPV] 81.08%, negative predictive value [NPV] 90.70%). The strain ratio is more accurate than conventional B-mode EBUS modalities for differentiating between malignant and benign lymph nodes. CONCLUSIONS:EBUS-guided elastography with strain ratio assessment can distinguish malignant from benign mediastinal lymph nodes with greater accuracy than conventional EBUS modalities. This new method may reduce the number of mediastinal EBUS-TBNAs and thus reduce the invasiveness and expense of mediastinal staging in patients with non-small lung cancer (NSCLC).

SUBMITTER: Rozman A

PROVIDER: S-EPMC4722923 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature

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Endobronchial ultrasound elastography strain ratio for mediastinal lymph node diagnosis.

Rozman Ales A Malovrh Mateja Marc MM Adamic Katja K Subic Tjasa T Kovac Viljem V Flezar Matjaz M

Radiology and oncology 20151127 4

<h4>Background</h4>Ultrasound elastography is an imaging procedure that can assess the biomechanical characteristics of different tissues. The aim of this study was to define the diagnostic value of the endobronchial ultrasound (EBUS) elastography strain ratio of mediastinal lymph nodes in patients with a suspicion of lung cancer. The diagnostic values of the strain ratios were compared with the EBUS brightness mode (B-mode) features of selected mediastinal lymph nodes and with their cytological ...[more]

PMID: 26834519

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Project description:BackgroundEndobronchial ultrasound (EBUS)-guided biopsy is the mainstay for investigation of mediastinal lymphadenopathy for laboratory diagnosis of malignancy, sarcoidosis, or TB. However, improved methods for discriminating between TB and sarcoidosis and excluding malignancy are still needed. We sought to evaluate the role of genomewide transcriptional profiling to aid diagnostic processes in this setting.MethodsMediastinal lymph node samples from 88 individuals were obtained by EBUS-guided aspiration for investigation of mediastinal lymphadenopathy and subjected to transcriptional profiling in addition to conventional laboratory assessments. Computational strategies were used to evaluate the potential for using the transcriptome to distinguish between diagnostic categories.ResultsMolecular signatures associated with granulomas or neoplastic and metastatic processes were clearly discernible in granulomatous and malignant lymph node samples, respectively. Support vector machine (SVM) learning using differentially expressed genes showed excellent sensitivity and specificity profiles in receiver operating characteristic curve analysis with area under curve values > 0.9 for discriminating between granulomatous and nongranulomatous disease, TB and sarcoidosis, and between cancer and reactive lymphadenopathy. A two-step decision tree using SVM to distinguish granulomatous and nongranulomatous disease, then between TB and sarcoidosis in granulomatous cases, and between cancer and reactive lymphadenopathy in nongranulomatous cases, achieved > 90% specificity for each diagnosis and afforded greater sensitivity than existing tests to detect TB and cancer. In some diagnostically ambiguous cases, computational classification predicted granulomatous disease or cancer before pathologic abnormalities were evident.ConclusionsMachine learning analysis of transcriptional profiling in mediastinal lymphadenopathy may significantly improve the clinical utility of EBUS-guided biopsies.

Project description:Background and Objectives: Endobronchial ultrasound elastography is a new technique for describing the stiffness of tissue during endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA). The aims of this study were to investigate the diagnostic value of Endobronchial ultrasound (EBUS) elastography for distinguishing the difference between benign and malignant lymph nodes among mediastinal and hilar lymph node. Materials and Methods: From June 2015 to August 2015, 47 patients confirmed of mediastinal and hilar lymph node enlargement through examination of Computed tomography (CT) were enrolled, and a total of 78 lymph nodes were evaluated by endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA). EBUS-guided elastography of lymph nodes was performed prior to EBUS-TBNA. A convex probe EBUS was used with a new EBUS processor to assess elastographic patterns that were classified based on color distribution as follows: Type 1, predominantly non-blue (green, yellow and red); Type 2, part blue, part non-blue (green, yellow and red); Type 3, predominantly blue. Pathological determination of malignant or benign lymph nodes was used as the gold standard for this study. The elastographic patterns were compared with the final pathologic results from EBUS-TBNA. Results: On pathological evaluation of the lymph nodes, 45 were benign and 33 were malignant. The lymph nodes that were classified as Type 1 on endobronchial ultrasound elastography were benign in 26/27 (96.3%) and malignant in 1/27 (3.7%); for Type 2 lymph nodes, 15/20 (75.0%) were benign and 5/20 (25.0%) were malignant; Type 3 lymph nodes were benign in 4/31 (12.9%) and malignant in 27/31 (87.1%). In classifying Type 1 as 'benign' and Type 3 as 'malignant,' the sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy rates were 96.43%, 86.67%, 87.10%, 96.30%, 91.38%, respectively. Conclusion: EBUS elastography of mediastinal and hilar lymph nodes is a noninvasive technique that can be performed reliably and may be helpful in the prediction of benign and malignant lymph nodes among mediastinal and hilar lymph node during EBUS-TBNA.

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:BackgroundEndobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is a widely used, safe, and accurate technique for obtaining pathological specimens to be used in the diagnosis of diseases involving lung hilar and mediastinal lymph node (LN) enlargement. However, application of the suction technique during EBUS-TBNA remains controversial. In addition, the effectiveness of the slow-pull capillary technique for the diagnosis of pancreatic masses was recently reported. The aim of this study was to compare the diagnostic accuracy of EBUS-TBNA using these two techniques.MethodsThe accuracy, sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and availability of tissue cores of the suction and slow-pull capillary techniques were studied retrospectively in patients who underwent EBUS-TBNA for the diagnosis of diseases involving lung hilar and mediastinal LN enlargement.ResultsA total of 97 patients with hilar and mediastinal LN enlargement underwent EBUS-TBNA; 30 patients underwent the suction technique, 56 patients underwent the slow-pull capillary technique, 5 patients underwent both techniques, and 6 patients had failed operations. The accuracy, sensitivity, specificity, NPV, PPV, and the number of tissue cores obtained with the suction and slow-pull capillary techniques were 66.67% versus 85.71% (p = 0.039), 43.75% versus 85.42% (p < 0.001), 92.86% versus 87.5% (p > 0.05), 59.09% versus 50% (p > 0.05), 87.5% versus 97.62% (p > 0.05), and 19 versus 50 (p = 0.004), respectively. In both univariate and multivariate analyses, the acquisition of tissue core was significantly associated with the diagnostic accuracy of EBUS-TBNA. Moreover, the slow-pull capillary technique was significantly associated with the acquisition of tissue core in EBUS-TBNA. There were no significant differences between the two groups in the blood contamination of samples.ConclusionsUse of the slow-pull capillary technique in EBUS-TBNA can significantly increase the accuracy related to the diagnosis of diseases involving hilar and mediastinal LN enlargement by improving the acquisition of tissue core. The reviews of this paper are available via the supplemental material section.

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Endobronchial ultrasound elastography strain ratio for mediastinal lymph node diagnosis.

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Endobronchial ultrasound elastography strain ratio for mediastinal lymph node diagnosis.

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