Development and validation of a nomogram for prediction of lymph node metastasis in early-stage breast cancer

Abstract

Background: Lymph node status is an important factor in determining the prognosis of early-stage breast cancer. We endeavored to build and validate a simple nomogram to predict lymph node metastasis (LNM) in patients with early-stage breast cancer.Methods: Patients with T1-2 and non-metastasis (M0) breast cancer registered in the Surveillance, Epidemiology, and End Results (SEER) database were enrolled. All patients were divided into primary cohort and validation cohort in a 2:1 ratio. In order to assess risk factors for LNM, we performed univariate and multivariate binary logistic regression, and based on results of multivariable analysis, we built the predictive nomogram model. The C-index, receiver operating characteristic (ROC) and calibration plots were applied to assess LNM model performance. Moreover, the nomogram efficiency was further validated through the validation cohort, part of which was from the First Affiliated Hospital of Nanjing Medical University database. Results: Totally, 184,531 female breast cancer with T1-2 tumor size from SEER database and 1,222 patients from the Chinese institutional data were included. There were 123,019 patients in the primary cohort and 62,734 patients in validation cohort. The LNM nomogram was composed of seven features including age at diagnosis, race, primary site, histologic type, grade, tumor size and subtype. The model showed good discrimination, with a C-index of 0.720 [95% confidence interval (CI): 0.717-0.723] and good calibration. Similar C-index was 0.718 (95% CI: 0.713-0.723) in validation cohort. Consistently, ROC curves presented good discrimination in the primary cohort [area under the curve (AUC) =0.720] and the validation set (AUC =0.718) for the LNM nomogram. Calibration curve of the nomogram demonstrated good agreement. Conclusions: With the prediction of novel validated nomogram for women with early-stage breast cancer, doctors may distinguish patients with high possibility of LNM and devise individualize treatments.

Development and validation of an extended Cox prognostic model for patients with ER/PR+ and HER2− breast cancer: a retrospective cohort study

“…Therefore, the prognostic value of the Ki67 index in breast cancer needs to be further explored. The cutoff value for age (41 and 55 years) was consistent with the age range of perimenopausal and menopausal Chinese women, although it was different from those of other studies [ 8 , 13 , 15 , 23 , 27 ]. Hence, our models showed high consistency to the currently available gold standard, such as the PR factor.…”

“…The test is performed in approximately 20 to 100% of patients with breast cancer at different hospitals in China; the instances where 100% of breast cancer patients were tested were mainly clinical trials. Furthermore, recently, the prognostic value of the classic clinicopathologic variables is being reconsidered [ 3 – 8 ]. Some evidence indicates that clinicopathologic variable models are excellent surrogates for prognostic gene signatures [ 3 , 5 ].…”

Is sentinel lymph node biopsy without frozen section in early stage breast cancer sufficient in accordance with ACOSOG-Z0011? A retrospective review from King Chulalongkorn Memorial Hospital

“…The nomogram identified 8 clinicopathological variables that were associated with SLN positivity: age, tumor size, tumor type, tumor location, lymphovascular invasion, multifocality, estrogen receptor status and progesterone receptor status [ 21 ]. Other studies supported the MSKCC nomogram findings that age, tumor size, histopathology, estrogen receptor status and progesterone receptor status were valuable predictors of SLN status [ 22 , 23 ]. A study from Thailand demonstrated that the MSKCC nomogram could accurately predict the probability of SLN metastasis for Thai breast cancer patients, however, only tumor size, histopathology, location, lymphovascular invasion, multifocality and progesterone receptor status were found to be significantly associated with SLNs metastasis [ 24 ].…”

Noninvasive Staging of Lymph Node Status (NILS) in Breast Cancer using Machine Learning: External Validation and Further Model Development

“…Multiple recent studies have presented prediction models for noninvasive staging of axillary nodal (N) status with the long-term aim of replacing SLNB for subgroups of patients with breast cancer [5][6][7][8][9][10][11][12][13][14][15][16][17]. Only routinely and preoperatively available data should be used for a feasible noninvasive diagnosis of axillary N status aimed at clinical implementation.…”