Tomlinson, GS;
Thomas, N;
Chain, BM;
Best, K;
Simpson, N;
Hardavella, G;
Brown, J;
... Noursadeghi, M; + view all
(2016)
Transcriptional profiling of endobronchial ultrasound guided lymph node samples aids diagnosis of mediastinal lymphadenopathy.
Chest
, 149
(2)
pp. 535-544.
10.1378/chest.15-0647.
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Abstract
Background: Endobronchial ultrasound (EBUS) guided biopsy is the mainstay for investigation of mediastinal lymphadenopathy for laboratory diagnosis of malignancy, sarcoidosis or tuberculosis. However, improved methods for discriminating between tuberculosis and sarcoidosis and excluding malignancy are still needed. We sought to evaluate the role of genome-wide transcriptional profiling to aid diagnostic processes in this setting. Methods: Mediastinal 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 employed to evaluate the potential for using the transcriptome to distinguish between diagnostic categories. Results: Molecular 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 non-granulomatous disease, tuberculosis and sarcoidosis, and between cancer and reactive lymphadenopathy. A two-step decision tree using SVM to distinguish granulomatous and non-granulomatous disease, then between tuberculosis and sarcoidosis in granulomatous cases and between cancer and reactive lymphadenopathy in non-granulomatous cases achieved >90% specificity for each diagnosis and afforded greater sensitivity than existing tests to detect tuberculosis and cancer. In some diagnostically ambiguous cases computational classification predicted granulomatous disease or cancer before pathological abnormalities were evident. Conclusions: Machine learning analysis of transcriptional profiling in mediastinal lymphadenopathy may significantly improve the clinical utility of EBUS guided biopsies.
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