Background: Endobronchial ultrasound (EBUS) guided biopsy is the mainstay for investigation of @entity3329 for laboratory diagnosis of @entity5 , @entity370 or @entity378 . However, improved methods for discriminating between @entity378 and @entity370 and excluding @entity5 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 @entity3329 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 @entity738 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 @entity738 , @entity378 and @entity370 , and between @entity5 and reactive @entity3329 . A two-step decision tree using SVM to distinguish @entity738 , then between @entity378 and @entity370 in granulomatous cases and between @entity5 and reactive @entity3329 in non-granulomatous cases achieved >90% specificity for each diagnosis and afforded greater sensitivity than existing tests to detect @entity378 and @entity5 . In some diagnostically ambiguous cases computational classification predicted @entity738 or @entity5 before pathological abnormalities were evident. Conclusions: Machine learning analysis of transcriptional profiling in @entity3329 may significantly improve the clinical utility of EBUS guided biopsies.

Transcriptional profiling of endobronchial ultrasound guided lymph node samples aids diagnosis of XXXX .

@entity3329 :: ('MESH:D008206', 'Disease') :: ['lymphadenopathy', 'mediastinal lymphadenopathy'] @entity738 :: ('MESH:D006099', 'Disease') :: ['granulomas', 'granulomatous disease', 'granulomatous and non-granulomatous disease'] @entity5 :: ('MESH:D009369', 'Disease') :: ['cancer', 'malignancy'] @entity370 :: ('MESH:D012507', 'Disease') :: ['sarcoidosis'] @entity378 :: ('MESH:D014376', 'Disease') :: ['tuberculosis']
@entity3329 :: (MESH:D008206,Disease) :: ['mediastinal lymphadenopathy']
