Evidence map›Paper›PMID 35562619›Full record

ArticleBreast cancer research and treatment2022

CBCRisk-Black: a personalized contralateral breast cancer risk prediction model for black women.

Ibrahim Hossain Sajal, Marzana Chowdhury, Tingfang Wang, David Euhus, Pankaj K Choudhary, Swati Biswas

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

Article in Breast cancer research and treatment, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
0.4field-weighted citation impact, top 47% 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

2 citing papers in PubMed, 1 synthesis or guideline pooled it, 5 citations in OpenAlex.

  1. Pooled it
  2. 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

6 authors at 3 institutions in 1 country.

Ibrahim Hossain SajalDepartment of Mathematical Sciences, University of Texas at Dallas, 800 W Campbell Rd, FO 35, Richardson, TX, 75080, USA.
Marzana ChowdhuryPerelman School of Medicine, University of Pennsylvania, Philadelphia, USA.
Tingfang WangDepartment 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.
Pankaj K ChoudharyDepartment of Mathematical Sciences, University of Texas at Dallas, 800 W Campbell Rd, FO 35, Richardson, TX, 75080, USA. pankaj@utdallas.edu.
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
The University of Texas at Dallas · USJohns Hopkins University · USUniversity of Pennsylvania · US

Funding

Risk-based Imaging Strategies to Improve Breast Cancer Surveillance OutcomesP01CA154292 · NCI · UNIVERSITY OF CALIFORNIA AT DAVIS · PI KARLA M KERLIKOWSKE · 2011 to 2026
$52.9M
A Model for Individualized Risk Prediction of Contralateral Breast CancerR21CA186086 · NCI · UNIVERSITY OF TEXAS DALLAS · PI BISWAS, SWATI, CHOUDHARY, PANKAJ · 2014 to 2015
$412k
NCI NIH HHS HHSN261201100031CNCI NIH HHS P01 CA154292NCI NIH HHS R21 CA186086
6 · The paper itself

Abstract

purposeBlack breast cancer (BC) survivors have a higher risk of developing contralateral breast cancer (CBC) than Whites. Existing CBC risk prediction tools are developed based on mostly White women. To address this racial disparity, it is crucial to develop tools tailored for Black women to help them inform about their actual risk of CBC.

methodsWe propose an absolute risk prediction model, CBCRisk-Black, specifically for Black BC patients. It uses data on Black women from two sources: Breast Cancer Surveillance Consortium (BCSC) and Surveillance, Epidemiology, and End Results (SEER). First, a matched lasso logistic regression model for estimating relative risks (RR) is developed. Then, it is combined with relevant hazard rates and attributable risks to obtain absolute risks. Six-fold cross-validation is used to internally validate CBCRisk-Black. We also compare CBCRisk-Black with CBCRisk, an existing CBC risk prediction model.

resultsThe RR model uses data from BCSC on 744 Black women (186 cases). CBCRisk-Black has four risk factors (RR compared to baseline): breast density (2.13 for heterogeneous/extremely dense), family history of BC (2.28 for yes), first BC tumor size (2.14 for T3/T4, 1.56 for TIS), and age at first diagnosis of BC (1.41 for < 40). The area under the receiver operating characteristic curve (AUC) for 3- and 5-year predictions are 0.72 and 0.65 for CBCRisk-Black while those are 0.65 and 0.60 for CBCRisk.

conclusionCBCRisk-Black may serve as a useful tool to clinicians in counseling Black BC patients by providing a more accurate and personalized CBC risk estimate.

Indexed as

Breast NeoplasmsCancer SurvivorsBlack PeopleBreast DensityFemaleHumansRisk FactorsBCSCBlackCBCRiskContralateral breast cancerLassoRacial disparityRisk prediction

Identifiers

PMID35562619
PMCPMC9637387
OpenAlexW4280495265

What OpenQuestion holds

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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.