Nomogram predicts survival benefit from preoperative radiotherapy for non-metastatic breast cancer: A SEER-based study
Metrics: PDF 1097 views | HTML 1510 views | ?
Jianjun Liu1,*, Mingxue Su2,*, Shikai Hong1,*, Hong Gao1, Xucai Zheng1 and Shengying Wang1
1Department of Head, Neck, and Breast Surgery, Anhui Provincial Cancer Hospital, West Branch of Anhui Provincial Hospital, Hefei, China
2Department of Infectious Disease Epidemiology, Lu’an People’s Hospital, Lu’an, China
*These authors contributed equally to this work
Shengying Wang, email: Shengywang@163.com
Keywords: nomogram, breast cancer, preoperative radiotherapy
Received: February 22, 2017 Accepted: May 01, 2017 Published: May 18, 2017
Background: To estimate survival in non-metastatic breast cancer patients who failed to achieve a pathological complete response (pCR) more effectively, we combined the clinicpathological characteristics after preoperative radiation therapy (pRT) and established a novel nomogram.
Materials and Methods: Using the Surveillance, Epidemiology, and End Results (SEER) database, we identified 2,545 non-metastatic breast cancer patients who underwent pRT between 1998 and 2013. Based on the registries of patients, the primary cohort divided into training set (n = 1,692) and validation set (n = 853). Nomograms were established by training set and validated by validation set.
Results: According to the multivariate analysis of training set, nomogram which combined age at diagnosed, marital status, location, grade, ER status, yp-T status, yp-N status and whether received breast conservation surgery (BCS) was developed. Calibration plots of the nomograms showed that the probability of DSS corresponded to actual observation closely. The C-index was 0.78 in validation set, which was significantly higher than that of yp-TNM staging system (0.75, p = 0.004).
Conclusions: The proposed nomogram resulted in more–reliable DSS prediction for non-metastatic breast cancer patients in general population, it would be helpful in individualized survival prediction and better treatment allocation after pRT.
All site content, except where otherwise noted, is licensed under a Creative Commons Attribution 3.0 License.