Evidence map›Paper›PMID 25562183›Full record

ArticleAnnals of the American Thoracic Society2015

Diagnosing lung carcinomas with optical coherence tomography.

Lida P Hariri, Mari Mino-Kenudson, Michael Lanuti, Alyssa J Miller, Eugene J Mark, Melissa J Suter

Open access · greenAbstract read
In one paragraph

Article in Annals of the American Thoracic Society, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 1 pooled it
4.0field-weighted citation impact, top 6% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

27 citing papers in PubMed, 1 synthesis or guideline pooled it, 75 citations in OpenAlex.

  1. The clinical usefulness of optical coherence tomography during cancer interventions.Journal of cancer research and clinical oncology · 2018
    Pooled it
  2. Article
  3. Review
  4. Review
  5. Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Optical coherence tomography.Nature reviews. Methods primers · 2022
    Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Ten Years of Gabor-Domain Optical Coherence Microscopy.Applied sciences (Basel, Switzerland) · 2019
    Article
  17. Biomedical optics express · 2019
    Article
  18. Article
  19. Needle-based Optical Coherence Tomography to Guide Transbronchial Lymph Node Biopsy.Journal of bronchology & interventional pulmonology · 2018
    Article
  20. Review of optical coherence tomography in oncology.Journal of biomedical optics · 2017
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 1 country.

Lida P Hariri1 Division of Pulmonary and Critical Care Medicine, Department of Medicine.
Mari Mino-Kenudson
Michael Lanuti
Alyssa J Miller
Eugene J Mark
Melissa J Suter
Harvard University · USPulmonary and Critical Care Associates · US

Funding

MOLECULAR IMMUNOLOGY AND TUMOR BIOLOGYT32CA009216 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI David Michael Langenau · 1985 to 2026
$10.6M
Increasing the diagnostic yield of low-risk bronchial biopsy in the evaluation ofR01CA167827 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI BOUMA, BRETT E, SUTER, MELISSA J · 2013 to 2017
$2.7M
Optical Imaging of the Pulmonary Airways in the Assessment of Lung CancerR00CA134920 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI SUTER, MELISSA J · 2010 to 2012
$732k
NCI NIH HHS R00 CA134920NCI NIH HHS R00CA134920NCI NIH HHS R01 CA167827NCI NIH HHS R01CA167827NCI NIH HHS T32 CA009216NCI NIH HHS T32CA009216
6 · The paper itself

Abstract

rationaleLung carcinoma diagnosis on tissue biopsy can be challenging because of insufficient tumor and lack of architectural information. Optical coherence tomography (OCT) is a high-resolution imaging modality that visualizes tissue microarchitecture in volumes orders of magnitude larger than biopsy. It has been proposed that OCT could potentially replace tissue biopsy.

objectivesWe aim to determine whether OCT could replace histology in diagnosing lung carcinomas. We develop and validate OCT interpretation criteria for common primary lung carcinomas: adenocarcinoma, squamous cell carcinoma (SCC), and poorly differentiated carcinoma.

methodsA total of 82 ex vivo tumor samples were included in a blinded assessment with 3 independent readers. Readers were trained on the OCT criteria, and applied these criteria to diagnose adenocarcinoma, SCC, or poorly differentiated carcinoma in an OCT validation dataset. After a 7-month period, the readers repeated the training and validation dataset interpretation. An independent pathologist reviewed corresponding histology. MEASUREMENTS AND MAIN

resultsThe average accuracy achieved by the readers was 82.6% (range, 73.7-94.7%). The sensitivity and specificity for adenocarcinoma were 80.3% (65.7-91.4%) and 88.6% (80.5-97.6%), respectively. The sensitivity and specificity for SCC were 83.3% (70.0-100.0%) and 87.0% (75.0-96.5%), respectively. The sensitivity and specificity for poorly differentiated carcinoma were 85.7% (81.0-95.2%) and 97.6% (92.9-100.0%), respectively.

conclusionsAlthough these results are encouraging, they indicate that OCT cannot replace histology in the diagnosis of lung carcinomas. However, OCT has potential to aid in diagnosing lung carcinomas as a complement to tissue biopsy, particularly when insufficient tissue is available for pathology assessment.

Indexed as

Tomography, Optical CoherenceAdenocarcinomaCarcinoma, Squamous CellHumansLung NeoplasmsReproducibility of ResultsSensitivity and Specificityin vivo microscopylung canceroptical biopsy guidanceoptical frequency domain imagingtransbronchial fine needle aspiration guidance

Identifiers

PMID25562183
PMCPMC4342833
OpenAlexW2065522317

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.