Evidence map›Paper›PMID 29574637›Full record

ArticleBreast cancer research and treatment2018

Validation of a personalized risk prediction model for contralateral breast cancer.

Marzana Chowdhury, David Euhus, Banu Arun, Chris Umbricht, Swati Biswas, Pankaj Choudhary

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Breast cancer research and treatment, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
1.8field-weighted citation impact, top 14% 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

13 citing papers in PubMed, 26 citations in OpenAlex.

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  5. EBCC-13 manifesto: Balancing pros and cons for contralateral prophylactic mastectomy.European journal of cancer (Oxford, England : 1990) · 2023
    Review
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  7. Tools for Contralateral Prophylactic Mastectomy Decision Making.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2022
    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

6 authors at 3 institutions in 1 country.

Marzana ChowdhuryDepartment of Mathematical Sciences, University of Texas at Dallas, 800 W Campbell Rd., FO 35, Richardson, TX, 75080, USA.
David EuhusDivision of Surgical Oncology, Johns Hopkins University, Baltimore, USA.
Banu ArunDepartment of Breast Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, USA.
Chris UmbrichtDivision of Surgical Oncology, Johns Hopkins University, Baltimore, USA.
Swati BiswasDepartment of Mathematical Sciences, University of Texas at Dallas, 800 W Campbell Rd., FO 35, Richardson, TX, 75080, USA. swati.biswas@utdallas.edu.ORCID http://orcid.org/0000-0001-7758-4460
Pankaj ChoudharyDepartment of Mathematical Sciences, University of Texas at Dallas, 800 W Campbell Rd., FO 35, Richardson, TX, 75080, USA. pankaj@utdallas.edu.
The University of Texas at Dallas · USJohns Hopkins University · USThe University of Texas MD Anderson Cancer Center · US

Funding

Multicenter Genetic, Epigenetic & Expression Analysis of DCIS outcome predictorsR01CA140311 · NCI · JOHNS HOPKINS UNIVERSITY · PI UMBRICHT, CHRISTOPHER B · 2011 to 2015
$2.6M
A Model for Individualized Risk Prediction of Contralateral Breast CancerR21CA186086 · NCI · UNIVERSITY OF TEXAS DALLAS · PI BISWAS, SWATI, CHOUDHARY, PANKAJ · 2014 to 2015
$412k
Division of Cancer Epidemiology and Genetics, National Cancer Institute R21CA186086NCI NIH HHS R01 CA140311NCI NIH HHS R21 CA186086
6 · The paper itself

Abstract

purposeWomen diagnosed with unilateral breast cancer are increasingly choosing to remove their other unaffected breast through contralateral prophylactic mastectomy (CPM) to reduce the risk of contralateral breast cancer (CBC). Yet a large proportion of CPMs are believed to be medically unnecessary. Thus, there is a pressing need to educate patients effectively on their CBC risk. We had earlier developed a CBC risk prediction model called CBCRisk based on eight personal risk factors.

methodsIn this study, we validate CBCRisk on independent clinical data from the Johns Hopkins University (JH) and MD Anderson Cancer Center (MDA). Women whose first breast cancer diagnosis was either invasive and/or ductal carcinoma in situ and whose age at first diagnosis was between 18 and 88 years were included in the cohorts because CBCRisk was developed specifically for these women. A woman who develops CBC is called a case whereas a woman who does not is called a control. The cohort sizes are 6035 (with 117 CBC cases) for JH and 5185 (with 111 CBC cases) for MDA. We computed the relevant calibration and validation measures for 3- and 5-year risk predictions.

resultsWe found that the model performs reasonably well for both cohorts. In particular, area under the receiver-operating characteristic curve for the two cohorts range from 0.61 to 0.65.

conclusionsWith this independent validation, CBCRisk can be used confidently in clinical settings for counseling BC patients by providing their individualized CBC risk. In turn, this may potentially help alleviate the rate of medically unnecessary CPMs.

Indexed as

Models, TheoreticalAdultBreast NeoplasmsCase-Control StudiesFemaleHumansMiddle AgedProphylactic MastectomyReproducibility of ResultsRisk AssessmentRisk FactorsROC CurveSensitivity and SpecificityYoung AdultAbsolute riskCBCRiskContralateral breast cancerContralateral prophylactic mastectomy

Identifiers

PMID29574637
PMCPMC6331647
OpenAlexW2792852094

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.