Prognostic Factor Analysis and Model Construction of Triple-Negative Metaplastic Breast Carcinoma After Surgery - Zhu, Keying; Chen, Yuyuan; Guo, Rong; Dai, Lanyi; Wang, Jiankui; Tang, Yiyin; Zhou, Shaoqiang; Chen, Dedian; Huang, Sheng (Frontiers in Oncology, 2022)
Objective: To analyze the prognostic factors of patients with triple-negative (TN) metaplastic breast carcinoma (MpBC) after surgery and to construct a nomogram for forecasting the 3-, 5- and 8-year overall survival (OS). Methods：998 patients extracted from the Surveillance, Epidemiology and End Results (SEER) database were assigned into either the training or validation group at random in a ratio of 7:3. The clinical characteristics of patients in the training and validation sets were compared, and multivariate Cox regression analysis was used to identify the independent risk variables for the OS of patients with TN MpBC after surgery. These selected parameters were estimated through the Kaplan–Meier (KM) curves using the log-rank test. The nomogram for predicting the OS were constructed and validated by performing the concordance index (C-index), receiver operating characteristics (ROC) curves with area under the receiver operating characteristic curves (AUC), calibration curves, and decision curve analyses (DCAs). Patients were then stratified as high risk and low risk, and KM curves were performed. Results: Multivariate Cox regression analysis indicated that factors including age, marital status, clinical stage at diagnosis, chemotherapy, and regional node status were independent predictors of prognosis in patients with MpBC after surgery. Separate KM curves for the screened variables revealed the same statistical results with Cox regression analysis. A prediction model was created and virtualized via nomogram based on these findings. For the training and validation cohorts, the C-index of the nomogram was 0.730 and 0.719, respectively. The AUC value of the 3-, 5-, 8-year OS were 0.758, 0.757, and 0.785 in the training group, and 0.736, 0.735, and 0.736 for in the validation group, respectively. The OS between the real observation and the forecast was constant according to the calibration curves. The generated nomogram’s clinical applicability was further demonstrated by the DCA analysis. The KM curves for the different risk subgroups revealed substantial differences in survival probabilities (P < 0.001). Conclusion: The study showed a nomogram that was built from a parametric survival model based on the SEER database, which can be used to make an accurate prediction of the prognosis of TN MpBC patients after surgery.