Evidence map›Paper›PMID 37277788›Full record

ArticleBMC pulmonary medicine2023

The presence of circulating genetically abnormal cells in blood predicts risk of lung cancer in individuals with indeterminate pulmonary nodules.

Shahram Tahvilian, Joshua D Kuban, David F Yankelevitz, Daniel Leventon, Claudia I Henschke, Jeffrey Zhu, Lara Baden, Rowena Yip, Fred R Hirsch, Rebecca Reed and 6 more

Open access · goldAbstract read
In one paragraph

Article in BMC pulmonary medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
2.1field-weighted citation impact, top 12% 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

5 citing papers in PubMed, 8 citations in OpenAlex.

  1. Review
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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

16 authors at 4 institutions in 1 country.

Shahram TahvilianLungLife AI, Inc, 2545 W. Hillcrest Drive, Suite 140, Thousand Oaks, CA, USA.
Joshua D KubanDepartment of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
David F YankelevitzDepartment of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Daniel LeventonLungLife AI, Inc, 2545 W. Hillcrest Drive, Suite 140, Thousand Oaks, CA, USA.
Claudia I HenschkeDepartment of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Jeffrey ZhuDepartment of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Lara BadenLungLife AI, Inc, 2545 W. Hillcrest Drive, Suite 140, Thousand Oaks, CA, USA.
Rowena YipDepartment of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Fred R HirschIcahn School of Medicine, Center for Thoracic Oncology, Tisch Cancer Institute at Mount Sinai, New York, NY, USA.
Rebecca ReedLungLife AI, Inc, 2545 W. Hillcrest Drive, Suite 140, Thousand Oaks, CA, USA.
Ashley BrownLungLife AI, Inc, 2545 W. Hillcrest Drive, Suite 140, Thousand Oaks, CA, USA.
Allison MuldoonLungLife AI, Inc, 2545 W. Hillcrest Drive, Suite 140, Thousand Oaks, CA, USA.
Michael TrejoLungLife AI, Inc, 2545 W. Hillcrest Drive, Suite 140, Thousand Oaks, CA, USA.
Benjamin A KatchmanLungLife AI, Inc, 2545 W. Hillcrest Drive, Suite 140, Thousand Oaks, CA, USA.
Michael J DonovanLungLife AI, Inc, 2545 W. Hillcrest Drive, Suite 140, Thousand Oaks, CA, USA.
Paul C PaganoLungLife AI, Inc, 2545 W. Hillcrest Drive, Suite 140, Thousand Oaks, CA, USA. ppagano@lunglifeai.com.
LungLife AI (United States) · USIcahn School of Medicine at Mount Sinai · USThe University of Texas MD Anderson Cancer Center · USTisch Cancer Institute

Funding

THE TISCH CANCER INSTITUTE - CANCER CENTER SUPPORT GRANTP30CA196521 · NCI · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI Deanna L Benson · 2015 to 2026
$35.4M
NCI NIH HHS P30 CA196521
6 · The paper itself

Abstract

purposeComputed tomography is the standard method by which pulmonary nodules are detected. Greater than 40% of pulmonary biopsies are not lung cancer and therefore not necessary, suggesting that improved diagnostic tools are needed. The LungLB™ blood test was developed to aid the clinical assessment of indeterminate nodules suspicious for lung cancer. LungLB™ identifies circulating genetically abnormal cells (CGACs) that are present early in lung cancer pathogenesis.

methodsLungLB™ is a 4-color fluorescence in-situ hybridization assay for detecting CGACs from peripheral blood. A prospective correlational study was performed on 151 participants scheduled for a pulmonary nodule biopsy. Mann-Whitney, Fisher's Exact and Chi-Square tests were used to assess participant demographics and correlation of LungLB™ with biopsy results, and sensitivity and specificity were also evaluated.

resultsParticipants from Mount Sinai Hospital (n = 83) and MD Anderson (n = 68), scheduled for a pulmonary biopsy were enrolled to have a LungLB™ test. Additional clinical variables including smoking history, previous cancer, lesion size, and nodule appearance were also collected. LungLB™ achieved 77% sensitivity and 72% specificity with an AUC of 0.78 for predicting lung cancer in the associated needle biopsy. Multivariate analysis found that clinical and radiological factors commonly used in malignancy prediction models did not impact the test performance. High test performance was observed across all participant characteristics, including clinical categories where other tests perform poorly (Mayo Clinic Model, AUC = 0.52).

conclusionEarly clinical performance of the LungLB™ test supports a role in the discrimination of benign from malignant pulmonary nodules. Extended studies are underway.

Indexed as

Lung NeoplasmsMultiple Pulmonary NodulesSolitary Pulmonary NoduleBiopsyHumansLungProspective StudiesEarly detectionIndeterminate nodulesLiquid biopsyLung cancerPulmonary nodules

Identifiers

PMID37277788
PMCPMC10240808
OpenAlexW4379383821

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.