Evidence map›Paper›PMID 28199152›Full record

ArticleAJR. American journal of roentgenology2017

Can Radiologists Predict the Presence of Ductal Carcinoma In Situ and Invasive Breast Cancer?

Shadi Aminololama-Shakeri, Chris I Flowers, Christine E McLaren, Dorota J Wisner, Jade de Guzman, Joan E Campbell, Lawrence W Bassett, Haydee Ojeda-Fournier, Karen Gerlach, Jonathan Hargreaves and 6 more

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in AJR. American journal of roentgenology, 2017. 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
–field-weighted citation impact
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.

  1. Article
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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.

Shadi Aminololama-Shakeri1 Department of Radiology, University of California Davis, 4860 Y St, Ste 3100, Sacramento, CA 95817.
Chris I Flowers2 Jonsson Comprehensive Cancer Center, University of California, Los Angeles, CA.
Christine E McLaren4 Department of Epidemiology, University of California, Irvine, CA.
Dorota J Wisner5 Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA.
Jade de Guzman7 Department of Radiology, University of California, San Diego, CA.
Joan E Campbell8 Department of Radiological Sciences, University of California, Irvine, CA.
Lawrence W Bassett9 Breast Imaging Section, Department of Radiology, David Geffen School of Medicine, University of California, Los Angeles, CA.
Haydee Ojeda-Fournier7 Department of Radiology, University of California, San Diego, CA.
Karen Gerlach8 Department of Radiological Sciences, University of California, Irvine, CA.
Jonathan Hargreaves1 Department of Radiology, University of California Davis, 4860 Y St, Ste 3100, Sacramento, CA 95817.
Sarah L Elson5 Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA.
Hanna Retallack5 Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA.
Bonnie N Joe5 Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA.
Stephen A Feig8 Department of Radiological Sciences, University of California, Irvine, CA.
Colin J Wells9 Breast Imaging Section, Department of Radiology, David Geffen School of Medicine, University of California, Los Angeles, CA.
ATHENA Breast Health Initiative

Funding

Univ.of Calif., Irvine Cancer Center Support GrantP30CA062203 · NCI · UNIVERSITY OF CALIFORNIA-IRVINE · PI Joel E Milam · 1994 to 2026
$57.9M
NCI NIH HHS P30 CA062203
6 · The paper itself

Abstract

objectiveWe hypothesize that radiologists' estimated percentage likelihood assessments for the presence of ductal carcinoma in situ (DCIS) and invasive cancer may predict histologic outcomes. MATERIALS AND

methodsTwo hundred fifty cases categorized as BI-RADS category 4 or 5 at four University of California Medical Centers were retrospectively reviewed by 10 academic radiologists with a range of 1-39 years in practice. Readers assigned BI-RADS category (1, 2, 3, 4a, 4b, 4c, or 5), estimated percentage likelihood of DCIS or invasive cancer (0-100%), and confidence rating (1 = low, 5 = high) after reviewing screening and diagnostic mammograms and ultrasound images. ROC curves were generated.

resultsSixty-two percent (156/250) of lesions were benign and 38% (94/250) were malignant. There were 26 (10%) DCIS, 20 (8%) invasive cancers, and 48 (19%) cases of DCIS and invasive cancer. AUC values were 0.830-0.907 for invasive cancer and 0.731-0.837 for DCIS alone. Sensitivity of 82% (56/68), specificity of 84% (153/182), positive predictive value (PPV) of 66% (56/85), negative predictive value (NPV) of 93% (153/165), and accuracy of 84% ([56 + 153]/250) were calculated using an estimated percentage likelihood of 20% or higher as the prediction threshold for invasive cancer for the radiologist with the highest AUC (0.907; 95% CI, 0.864-0.951). Every 20% increase in the estimated percentage likelihood of invasive cancer increased the odds of invasive cancer by approximately two times (odds ratio, 2.4). For DCIS, using a threshold of 40% or higher, sensitivity of 81% (21/26), specificity of 79% (178/224), PPV of 31% (21/67), NPV of 97% (178/183), and accuracy of 80% ([21 + 178]/250) were calculated. Similarly, these values were calculated at thresholds of 2% or higher (BI-RADS category 4) and 95% or higher (BI-RADS category 5) to predict the presence of malignancy.

conclusionUsing likelihood estimates, radiologists may predict the presence of invasive cancer with fairly high accuracy. Radiologist-assigned estimated percentage likelihood can predict the presence of DCIS, albeit with lower accuracy than that for invasive cancer.

Indexed as

AdultAgedAged, 80 and overBreast NeoplasmsCaliforniaCarcinoma, Ductal, BreastClinical CompetenceFemaleHumansMiddle AgedNeoplasm InvasivenessObserver VariationPrevalenceRadiologistsReproducibility of ResultsSensitivity and SpecificityBI-RADSbreast cancerdigital mammographyductal carcinoma in situinvasive breast cancerkappa coefficientsROC curves

Identifiers

PMID28199152
PMCPMC7777562

What OpenQuestion holds

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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.