AI Article Synopsis

  • Endobronchial ultrasound (EBUS)-guided biopsy is essential for diagnosing issues like malignancy, sarcoidosis, or tuberculosis in mediastinal lymph nodes, but better methods for distinguishing these conditions are needed.
  • Researchers tested genomewide transcriptional profiling to enhance diagnostic accuracy using samples from 88 patients.
  • The study found that machine learning could effectively differentiate between various diseases with over 90% specificity, even predicting conditions before traditional pathology showed abnormalities, indicating potential improvements in diagnosing complex cases.

Article Abstract

Background: Endobronchial 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.

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 used 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 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.

Conclusions: Machine learning analysis of transcriptional profiling in mediastinal lymphadenopathy may significantly improve the clinical utility of EBUS-guided biopsies.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4740456PMC
http://dx.doi.org/10.1378/chest.15-0647DOI Listing

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