Evidence map›Paper›PMID 39015697›Full record

ArticleGland surgery2024

Construction and validation of a nomogram for predicting the prognosis of breast cancer patients who received adjuvant therapy: an analysis based on the SEER database.

Qingliang Jiang, Xianglin Liu, Yuting Wu, Jiaqi Du, Yingying Rao, Jiayu Li, Hengyu Li

Abstract read
In one paragraph

Article in Gland surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Qingliang Jiang *Department of Hepatobiliary Surgery, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai, China.ORCID https://orcid.org/0000-0003-1793-8305
Xianglin Liu *Department of Breast and Thyroid Surgery, Changhai Hospital, Naval Medical University, Shanghai, China.
Yuting WuDepartment of Breast and Thyroid Surgery, Changhai Hospital, Naval Medical University, Shanghai, China.
Jiaqi DuDepartment of Breast and Thyroid Surgery, Changhai Hospital, Naval Medical University, Shanghai, China.
Yingying RaoDepartment of Breast and Thyroid Surgery, Changhai Hospital, Naval Medical University, Shanghai, China.
Jiayu LiDepartment of Breast and Thyroid Surgery, Changhai Hospital, Naval Medical University, Shanghai, China.
Hengyu LiDepartment of Breast and Thyroid Surgery, Changhai Hospital, Naval Medical University, Shanghai, China.ORCID https://orcid.org/0000-0002-1007-6493

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Breast cancer is the most common malignant tumor in women globally. Despite advances in primary treatment, the role of adjuvant therapy in reducing recurrence and improving survival is critical; however, there is a notable lack of tailored prognostic models for patients receiving adjuvant therapy. This study used the Surveillance, Epidemiology, and End Results (SEER) database to develop a prognostic nomogram for breast cancer patients receiving adjuvant therapy. Methods: The data of breast cancer patients who received adjuvant therapy after surgery in 2014-2015 were extracted from the SEER database. Univariate Cox regression identified significant prognostic variables that were further refined by least absolute shrinkage and selection operator (LASSO) regression and cross-validation analyses. These variables were incorporated into a multivariate Cox regression analysis to establish the predictive model. This model was visualized and validated using various statistical measures. Results: A total of 54,960 patients were included in the study, with 38,472 in the training set and 16,488 in the validation set. Age, sex, race, marital status, grade, tumor (T) stage, lymph node (N) stage, subtype, and radiotherapy were found to be significant independent risk factors of 1-, 3-, and 5-year overall survival (OS). The receiver operating characteristic curve area for 1-, 3-, and 5-year OS was >0.76 in both sets. The consistency index values were 0.768 and 0.763 for the training and validation sets, respectively. The calibration curves showed good fit, and the nomogram exhibited substantial clinical utility. Conclusions: Incorporating various significant factors, the constructed nomogram was able to effectively predict the prognosis of breast cancer patients who received adjuvant therapy. This nomogram extends understandings of complex prognosis scenarios. In addition, it could enhance personalized treatment plans and assist in patient counseling.

Indexed as

adjuvant therapyBreast cancerprognosisSurveillance, Epidemiology, and End Results database (SEER database)

Identifiers

PMID39015697
PMCPMC11247573

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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.