Evidence map›Paper›PMID 33704778›Full record

ArticleCancer2021

Prediction models for breast cancer prognosis among Asian women.

Run Fan, Yufan Chen, Sarah Nechuta, Hui Cai, Kai Gu, Liang Shi, Pingping Bao, Yu Shyr, Xiao-Ou Shu, Fei Ye

Abstract read
In one paragraph

Article in Cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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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

10 authors.

Run FanDepartment of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee.
Yufan ChenDepartment of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee.
Sarah NechutaDepartment of Public Health, Grand Valley State University, Grand Rapids, Michigan.ORCID 0000-0003-2773-6678
Hui CaiDivision of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee.
Kai GuShanghai Municipal Center for Disease Control and Prevention, Shanghai, China.
Liang ShiShanghai Municipal Center for Disease Control and Prevention, Shanghai, China.
Pingping BaoShanghai Municipal Center for Disease Control and Prevention, Shanghai, China.
Yu ShyrDepartment of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee.
Xiao-Ou ShuDivision of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee.
Fei YeDepartment of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee.ORCID 0000-0001-6472-5076

Funding

Tumor Immunology and Microenvironment Research ProgramP30CA068485 · NCI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI MATTHEW J TYSKA · 1995 to 2026
$172.8M
TISSUE CoreP50CA098131 · NCI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI PARK, BEN HO, PIETENPOL, JENNIFER A · 2003 to 2024
$48.7M
Development and Evaluation of Prediction Models for Breast Cancer PrognosisR03CA178680 · NCI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI YE, FEI · 2014 to 2015
$157k
CSR NIH HHS P50 CA098131CSR NIH HHS R03 CA178680NCI NIH HHS P30 CA068485NCI NIH HHS P50 CA098131NCI NIH HHS R03 CA178680
6 · The paper itself

Abstract

backgroundRobust and reliable prognosis prediction models have not been developed and validated for Asian patients with breast cancer, a rapidly growing yet understudied population in the United States.

methodsWe used longitudinal data from the Shanghai Breast Cancer Survival Study, a population-based prospective cohort study (n = 5042), to develop prediction models for 5- and 10-year disease-free survival (DFS) and overall survival (OS). The initial models considered age at diagnosis, tumor grade, tumor size, number of positive nodes, TNM stage, chemotherapy, tamoxifen therapy, and estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) status. We then evaluated whether the addition of modifiable lifestyle factors (physical activity, soy isoflavones intake, and postdiagnostic weight change) improved the models. All final models have been validated internally and externally in the National Cancer Database when applicable.

resultsOur final models included age at diagnosis, tumor grade, tumor size, number of positive nodes, TNM stage, chemotherapy, tamoxifen therapy, ER status, PR status, 6-month postdiagnostic weight change, interaction between ER status and tamoxifen therapy, and interaction between age and TNM stage. The internal validation yielded C-statistics of 0.76, 0.74, 0.78, and 0.75 for 5-year DFS, 10-year DFS, 5-year OS, and 10-year OS, respectively. The external validation yielded C-statistics of 5- and 10-year OS both at 0.78 for Chinese ethnicity, 0.79 for East Asian ethnicity, and 0.75 and 0.76 for all ethnic groups combined.

conclusionWe developed prediction models for breast cancer prognosis from a large prospective study. Our prognostic models performed very well in women from the United States-particularly in Asian American women-and demonstrated high prediction accuracy and generalizability.

Indexed as

AsianBreast NeoplasmsModels, StatisticalDisease-Free SurvivalFemaleHumansPrognosisProspective StudiesReproducibility of ResultsUnited Statesbreast cancer prognosisdisease-free survivallifestyle factorsmodel validationoverall survivalprediction model

Identifiers

PMID33704778
PMCPMC9443412

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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.