Evidence map›Paper›PMID 32803166›Full record

ArticleRadiology. Imaging cancer2020

The Value of Patient and Tumor Factors in Predicting Preoperative Breast MRI Outcomes.

Habib Rahbar, Daniel S Hippe, Ahmed Alaa, Safia H Cheeney, Mihaela van der Schaar, Savannah C Partridge, Christoph I Lee

Open access · diamondAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Radiology. Imaging cancer, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.7field-weighted citation impact, top 27% 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

6 citing papers in PubMed, 10 citations in OpenAlex.

  1. Article
  2. Machine learning and new insights for breast cancer diagnosis.The Journal of international medical research · 2024
    Review
  3. Article
  4. Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors at 1 institution in 1 country.

Habib RahbarDepartment of Radiology, University of Washington School of Medicine, Seattle Cancer Care Alliance, 1144 Eastlake Ave E, Room LG2-211, Seattle, WA 98109 (H.R., D.S.H., S.H.C., S.C.P., C.I.L.); Department of Electrical and Computer Engineering, University of California, Los Angeles, Los Angeles, Calif (A.A., M.v.d.S.); and Departments of Applied Mathematics and Theoretical Physics and Public Health, University of Cambridge, Cambridge, England (M.v.d.S.).ORCID 0000-0003-4835-1478
Daniel S HippeDepartment of Radiology, University of Washington School of Medicine, Seattle Cancer Care Alliance, 1144 Eastlake Ave E, Room LG2-211, Seattle, WA 98109 (H.R., D.S.H., S.H.C., S.C.P., C.I.L.); Department of Electrical and Computer Engineering, University of California, Los Angeles, Los Angeles, Calif (A.A., M.v.d.S.); and Departments of Applied Mathematics and Theoretical Physics and Public Health, University of Cambridge, Cambridge, England (M.v.d.S.).ORCID 0000-0003-2427-4404
Ahmed AlaaDepartment of Radiology, University of Washington School of Medicine, Seattle Cancer Care Alliance, 1144 Eastlake Ave E, Room LG2-211, Seattle, WA 98109 (H.R., D.S.H., S.H.C., S.C.P., C.I.L.); Department of Electrical and Computer Engineering, University of California, Los Angeles, Los Angeles, Calif (A.A., M.v.d.S.); and Departments of Applied Mathematics and Theoretical Physics and Public Health, University of Cambridge, Cambridge, England (M.v.d.S.).
Safia H CheeneyDepartment of Radiology, University of Washington School of Medicine, Seattle Cancer Care Alliance, 1144 Eastlake Ave E, Room LG2-211, Seattle, WA 98109 (H.R., D.S.H., S.H.C., S.C.P., C.I.L.); Department of Electrical and Computer Engineering, University of California, Los Angeles, Los Angeles, Calif (A.A., M.v.d.S.); and Departments of Applied Mathematics and Theoretical Physics and Public Health, University of Cambridge, Cambridge, England (M.v.d.S.).
Mihaela van der SchaarDepartment of Radiology, University of Washington School of Medicine, Seattle Cancer Care Alliance, 1144 Eastlake Ave E, Room LG2-211, Seattle, WA 98109 (H.R., D.S.H., S.H.C., S.C.P., C.I.L.); Department of Electrical and Computer Engineering, University of California, Los Angeles, Los Angeles, Calif (A.A., M.v.d.S.); and Departments of Applied Mathematics and Theoretical Physics and Public Health, University of Cambridge, Cambridge, England (M.v.d.S.).
Savannah C PartridgeDepartment of Radiology, University of Washington School of Medicine, Seattle Cancer Care Alliance, 1144 Eastlake Ave E, Room LG2-211, Seattle, WA 98109 (H.R., D.S.H., S.H.C., S.C.P., C.I.L.); Department of Electrical and Computer Engineering, University of California, Los Angeles, Los Angeles, Calif (A.A., M.v.d.S.); and Departments of Applied Mathematics and Theoretical Physics and Public Health, University of Cambridge, Cambridge, England (M.v.d.S.).ORCID 0000-0001-6370-9111
Christoph I LeeDepartment of Radiology, University of Washington School of Medicine, Seattle Cancer Care Alliance, 1144 Eastlake Ave E, Room LG2-211, Seattle, WA 98109 (H.R., D.S.H., S.H.C., S.C.P., C.I.L.); Department of Electrical and Computer Engineering, University of California, Los Angeles, Los Angeles, Calif (A.A., M.v.d.S.); and Departments of Applied Mathematics and Theoretical Physics and Public Health, University of Cambridge, Cambridge, England (M.v.d.S.).
Seattle Cancer Care Alliance · US

Funding

Validation and Standardization of the Apparent Diffusion Coefficient as a Quantitative Imaging Biomarker in Breast CancerR01CA207290 · NCI · UNIVERSITY OF WASHINGTON · PI PARTRIDGE, SAVANNAH CORRINA · 2017 to 2022
$2.2M
MRI Characterization of Biological Risk of Ductal Carcinoma in SituR01CA203883 · NCI · UNIVERSITY OF WASHINGTON · PI RAHBAR, HABIBOLLAH · 2017 to 2021
$1.8M
NCI NIH HHS R01 CA203883NCI NIH HHS R01 CA207290
6 · The paper itself

Abstract

Purpose: To identify patient and tumor features that predict true-positive, false-positive, and negative breast preoperative MRI outcomes. Materials and Methods: Using a breast MRI database from a large regional cancer center, the authors retrospectively identified all women with unilateral breast cancer who underwent preoperative MRI from January 2005 to February 2015. A total of 1396 women with complete data were included. Patient features (ie, age, breast density) and index tumor features (ie, type, grade, hormone receptor, human epidermal growth factor receptor type 2/ Results: At the examination level, 181 additional cancers were identified among 1396 total preoperative MRI examinations (median patient age, 56 years; range, 25-94 years), resulting in a positive predictive value for biopsy of 43% (181 true-positive findings of 419 core-needle biopsies). In univariate analysis, no patient or tumor feature was associated with a true-positive outcome ( Conclusion: Commonly used patient and tumor factors driving expert opinion for the use of preoperative MRI provide limited predictive value for determining preoperative MRI outcomes in women.

Indexed as

Magnetic Resonance ImagingAdultAgedAged, 80 and overBreastBreast DensityBreast NeoplasmsFemaleHumansMiddle AgedRetrospective Studies

Identifiers

PMID32803166
PMCPMC7398118
OpenAlexW3042050807

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.