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Development and Validation of Nomograms for Predicting the Prognosis of Triple-Negative Breast Cancer Patients Based on 379 Chinese Patients

Authors Shi H, Wang XH, Gu JW, Guo GL

Received 16 October 2019

Accepted for publication 2 December 2019

Published 30 December 2019 Volume 2019:11 Pages 10827—10839

DOI https://doi.org/10.2147/CMAR.S234926

Checked for plagiarism Yes

Review by Single-blind

Peer reviewer comments 2

Editor who approved publication: Dr Antonella D'Anneo


Hao Shi, Xiao-Hui Wang, Jun-Wei Gu, Gui-Long Guo

Department of Thyroid & Breast Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, People’s Republic of China

Correspondence: Gui-Long Guo
Department of Thyroid & Breast Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325000, People’s Republic of China
Tel/Fax +86 577-13867709361
Email guoguilong@sina.com

Purpose: We aimed to construct universally applicable nomograms incorporating prognostic factors to predict the prognosis of patients with triple-negative breast cancer (TNBC).
Patients and methods: Clinicopathological data of 379 patients with TNBC from March 2008 to June 2014 were retrospectively collected and analyzed. The endpoints were disease-free survival (DFS) and overall survival (OS). Patients were randomly divided into a training group and an independent validation group. In the training group, the prognostic factors were screened to develop nomograms. C-index and calibration curves were used to evaluate the predictive accuracy and discriminative ability of nomograms in both groups. The accuracy of the nomograms was also compared with the traditional American Joint Committee on Cancer Tumor-Node-Metastasis anatomical stage (8th edition).
Results: Four prognostic factors (albumin-to-globulin ratio, neutrophil-to-lymphocyte ratio, positive lymph nodes, and tumor size) were used to construct the nomogram of DFS. In addition to the aforementioned factors, age was taken into account in the construction of the OS nomogram. The C-index of the DFS nomogram in the training and validation groups was 0.71 (95% confidence interval [CI]: 0.64–0.77) and 0.69 (95% CI: 0.58–0.79), respectively; the C-index of the OS nomogram was 0.77 (95% CI: 0.70–0.84) and 0.74 (95% CI: 0.62–0.86), respectively. This suggests that the nomograms had high accuracy. Moreover, calibration curves showed good consistencies in both groups. Our models showed superiority in predicting accuracy compared with the AJCC TNM staging system. Furthermore, two web pages of the nomograms were produced: DFS: https://sh-skipper.shinyapps.io/TNBC1/; OS: https://sh-skipper.shinyapps.io/TNBC2/.
Conclusion: These predictive models are simple and easy to use, particularly the web versions. They have certain clinical value in predicting the prognosis of patients with TNBC. They can assist doctors in identifying patients at different prognostic risks and strengthen the treatment or follow-up accordingly.

Keywords: triple negative breast cancer, prognosis, prediction, nomograms

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