Evidence map›Paper›PMID 36362854›Full record

ArticleLife (Basel, Switzerland)2022

Chest CT for Breast Cancer Diagnosis.

Elise Desperito, Lawrence Schwartz, Kathleen M Capaccione, Brian T Collins, Sachin Jamabawalikar, Boyu Peng, Rebecca Patrizio, Mary M Salvatore

Open access · goldAbstract read
In one paragraph

Article in Life (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
2.6field-weighted citation impact, top 8% 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

15 citing papers in PubMed, 26 citations in OpenAlex.

  1. Article
  2. Article
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  4. Article
  5. Review
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  8. Article
  9. Article
  10. Article
  11. Radiology of fibrosis. Part I: Thoracic organs.Journal of translational medicine · 2024
    Review
  12. Article
  13. Can Hounsfield units on chest CT characterize breast nodules as cystic or solid?Translational breast cancer research : a journal focusing on translational research in breast cancer · 2024
    Article
  14. Article
  15. Advances in PET/CT Imaging for Breast Cancer.Journal of clinical medicine · 2023
    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

8 authors at 2 institutions in 1 country.

Elise DesperitoDepartment of Radiology, Columbia University Irving Medical Center, New York, NY 10032, USA.
Lawrence SchwartzDepartment of Radiology, Columbia University Irving Medical Center, New York, NY 10032, USA.
Kathleen M CapaccioneDepartment of Radiology, Columbia University Irving Medical Center, New York, NY 10032, USA.
Brian T CollinsDepartment of Radiation Oncology, University of South Florida Tampa General Hospital, Tampa, FL 33612, USA.
Sachin JamabawalikarDepartment of Radiology, Columbia University Irving Medical Center, New York, NY 10032, USA.ORCID 0000-0003-1839-6798
Boyu PengDepartment of Radiology, Columbia University Irving Medical Center, New York, NY 10032, USA.ORCID 0000-0002-2487-877X
Rebecca PatrizioDepartment of Radiology, Columbia University Irving Medical Center, New York, NY 10032, USA.
Mary M SalvatoreDepartment of Radiology, Columbia University Irving Medical Center, New York, NY 10032, USA.
Columbia University Irving Medical Center · USUniversity of South Florida · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: We report the results of our retrospective analysis of the ability of standard chest CT scans to correctly diagnose cancer in the breast. Methods: Four hundred and fifty-three consecutive women with chest CT scans (contrast and non-contrast) preceding mammograms within one year comprise the study population. All chest CT images were reviewed by an experienced fellowship-trained chest radiologist and mammograms by an experienced fellowship-trained mammographer without the benefit of prior or ancillary studies; only four mammographic views were included for analysis. The size, location, and shape of breast masses were documented; on CT, the average Hounsfield units were measured. On both imaging modalities, the presence of lymphadenopathy, architectural distortion, skin thickening, and microcalcifications were recorded. Ultimately, the interpreting radiologist was asked to decide if a biopsy was indicated, and these recommendations were correlated with the patient’s outcome. Findings: Nineteen of four hundred and fifty-three patients had breast cancer at the time of the mammography. Breast masses were the most common finding on chest CT, leading to the recommendation for biopsy. Hounsfield units were the most important feature for discerning benign from malignant masses. CT sensitivity, specificity, and accuracy of CT for breast cancer detection was 84.21%, 99.3%, and 98.68% compared to 78.95%, 93.78%, and 93.16% for four-view mammography. Chest CT scans with or without contrast had similar outcomes for specificity and accuracy, but sensitivity was slightly less without contrast. Chest CT alone, without the benefit of prior exams and patient recall, correctly diagnosed cancer with a p-value of <0.0001 compared to mammography with the same limitations. Conclusion: Chest CT accurately diagnosed breast cancer with few false positives and negatives and did so without the need for patient recall for additional imaging.

Indexed as

breast cancerbreast densitycancer screeningchest CTmammography

Identifiers

PMID36362854
PMCPMC9695285
OpenAlexW4307254773

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.