Evidence map›Paper›PMID 36203704›Full record

ArticleFrontiers in public health2022

Development and validation of nomograms for predicting overall survival and cancer specific survival in locally advanced breast cancer patients: A SEER population-based study.

Fangxu Yin, Song Wang, Chong Hou, Yiyuan Zhang, Zhenlin Yang, Xiaohong Wang

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Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

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10citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Fangxu YinDepartment of Thyroid and Breast Surgery, Binzhou Medical University Hospital, Binzhou, China.
Song WangDepartment of Thyroid and Breast Surgery, Binzhou Medical University Hospital, Binzhou, China.
Chong HouDepartment of Gastroenterology, Yantai Affiliated Hospital of Binzhou Medical University, Yantai, China.
Yiyuan ZhangDepartment of Reproductive Endocrinology, Affiliated Reproductive Hospital of Shandong University, Jinan, China.
Zhenlin YangDepartment of Thyroid and Breast Surgery, Binzhou Medical University Hospital, Binzhou, China.
Xiaohong WangDepartment of Thyroid and Breast Surgery, Binzhou Medical University Hospital, Binzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: For patients with locally advanced breast cancer (LABC), conventional TNM staging is not accurate in predicting survival outcomes. The aim of this study was to develop two accurate survival prediction models to guide clinical decision making. Methods: A retrospective analysis of 22,842 LABC patients was performed from 2010 to 2015 using the Surveillance, Epidemiology and End Results (SEER) database. An additional cohort of 200 patients from the Binzhou Medical University Hospital (BMUH) was analyzed. The least absolute shrinkage and selection operator (LASSO) regression was used to screen for variables. The identified variables were used to build a survival prediction model. The performance of the nomogram models was assessed based on the concordance index (C-index), calibration plot, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA). Results: The LASSO analysis identified 9 variables in patients with LABC, including age, marital status, Grade, histological type, T-stage, N-stage, surgery, radiotherapy, and chemotherapy. In the training cohort, the C-index of the nomogram in predicting the overall survival (OS) was 0.767 [95% confidence intervals (95% CI): 0.751-0.775], cancer specific survival (CSS) was 0.765 (95% CI: 0.756-0.774). In the external validation cohort, the C-index of the nomogram in predicting the OS was 0.858 (95% CI: 0.812-0.904), the CSS was 0.866 (95% CI: 0.817-0.915). In the training cohort, the area under the receiver operator characteristics curve (AUC) values of the nomogram in prediction of the 1, 3, and 5-year OS were 0.836 (95% CI: 0.821-0.851), 0.769 (95% CI: 0.759-0.780), and 0.750 (95% CI: 0.738-0.762), respectively. The AUC values for prediction of the 1, 3, and 5-year CSS were 0.829 (95% CI: 0.811-0.847), 0.769 (95% CI: 0.757-0.780), and 0.745 (95% CI: 0.732-0.758), respectively. Results of the C-index, ROC curve, and DCA demonstrated that the nomogram was more accurate in predicting the OS and CSS of patients compared with conventional TNM staging. Conclusion: Two prediction models were developed and validated in this study which provided more accurate prediction of the OS and CSS in LABC patients than the TNM staging. The constructed models can be used for predicting survival outcomes and guide treatment plans for LABC patients.

Indexed as

Breast NeoplasmsNomogramsFemaleHumansPrognosisRetrospective StudiesSEER Programcancer specific survivallocally advanced breast cancernomogramoverall survivalprognosisSEER

Identifiers

PMID36203704
PMCPMC9530359

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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.