Evidence map›Paper›PMID 35280479›Full record

ArticleJournal of thoracic disease2022

Feasibility of a prototype carbon nanotube enabled stationary digital chest tomosynthesis system for identification of pulmonary nodules by pulmonologists.

Allen Cole Burks, Jason Akulian, Christina R MacRosty, Sohini Ghosh, Adam Belanger, Muthu Sakthivel, Thad S Benefield, Christina R Inscoe, Otto Zhou, Jianping Lu and 1 more

Open access · diamondAbstract read
In one paragraph

Article in Journal of thoracic disease, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.8field-weighted citation impact, top 29% 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

4 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Article
  3. Evolution of tomosynthesis.Journal of medical imaging (Bellingham, Wash.) · 2025
    Review
  4. Article
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

11 authors at 3 institutions in 1 country.

Allen Cole BurksDivision of Pulmonary and Critical Care Medicine, University of North Carolina at Chapel Hill, NC, USA.
Jason AkulianDivision of Pulmonary and Critical Care Medicine, University of North Carolina at Chapel Hill, NC, USA.
Christina R MacRostyDivision of Pulmonary and Critical Care Medicine, University of North Carolina at Chapel Hill, NC, USA.
Sohini GhoshDivision of Pulmonary and Critical Care, Allegheny Health Network, Pittsburgh, PA, USA.
Adam BelangerFirstHealth, Pinehurst Medical Clinic, Pinehurst, NC, USA.
Muthu SakthivelDepartment of Radiology, University of North Carolina at Chapel Hill, NC, USA.
Thad S BenefieldDepartment of Radiology, University of North Carolina at Chapel Hill, NC, USA.
Christina R InscoeDepartment of Physics and Astronomy, University of North Carolina at Chapel Hill, NC, USA.
Otto ZhouDepartment of Physics and Astronomy, University of North Carolina at Chapel Hill, NC, USA.
Jianping LuDepartment of Physics and Astronomy, University of North Carolina at Chapel Hill, NC, USA.
Yueh Z LeeDepartment of Radiology, University of North Carolina at Chapel Hill, NC, USA.
University of North Carolina at Chapel Hill · USAllegheny Health Network · USFirstHealth of the Carolinas · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Screen detected and incidental pulmonary nodules are increasingly common. Current guidelines recommend tissue sampling of solid nodules >8 mm. Bronchoscopic biopsy poses the lowest risk but is paired with the lowest diagnostic yield when compared to CT-guided biopsy or surgery. A need exists for a safe, mobile, low radiation dose, intra-procedural method to localize biopsy instruments within target nodules. This retrospective cross sectional reader feasibility study evaluates the ability of clinicians to identify pulmonary nodules using a prototype carbon nanotube radiation enabled stationary digital chest tomosynthesis system. Methods: Patients with pulmonary nodules on prior CT imaging were recruited and consented for imaging with stationary digital chest tomosynthesis. Five pulmonologists of varying training levels participated as readers. Following review of patient CT and a thoracic radiologist's interpretation of nodule size and location the readers were tasked with interpreting the corresponding tomosynthesis scan to identify the same nodule found on CT. Results: Fifty-five patients were scanned with stationary digital chest tomosynthesis. The median nodule size was 6 mm (IQR =4-13 mm). Twenty nodules (37%) were greater than 8 mm. The radiation entrance dose for s-DCT was 0.6 mGy. A significant difference in identification of nodules using s-DCT was seen for nodules <8 Conclusions: With system and carbon nanotube array optimization, we hypothesize the detection rate for nodules will improve. Additional study is needed to evaluate its use in target and tool co-localization and target biopsy.

Indexed as

bronchoscopychest imaginglung cancerpulmonary nodulesTomosynthesis

Identifiers

PMID35280479
PMCPMC8902128
OpenAlexW4213147723

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.