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Randomized Controlled Trial
. 2023 Dec 23;31(1):115-131.
doi: 10.3390/curroncol31010008.

A Nomogram and Risk Classification System Predicting the Prognosis of Patients with De Novo Metastatic Breast Cancer Undergoing Immediate Breast Reconstruction: A Surveillance, Epidemiology, and End Results Population-Based Study

Jingjing Zhao  1 , Shichang Bian  1 , Xu Di  1 , Chunhua Xiao  2
Affiliations
Randomized Controlled Trial

A Nomogram and Risk Classification System Predicting the Prognosis of Patients with De Novo Metastatic Breast Cancer Undergoing Immediate Breast Reconstruction: A Surveillance, Epidemiology, and End Results Population-Based Study

Jingjing Zhao et al. Curr Oncol. .

Abstract

Background The lifespan of patients diagnosed with de novo metastatic breast cancer (dnMBC) has been prolonged. Nonetheless, there remains substantial debate regarding immediate breast reconstruction (IBR) for this particular subgroup of patients. The aim of this study was to construct a nomogram predicting the breast cancer-specific survival (BCSS) of dnMBC patients who underwent IBR. Methods A total of 682 patients initially diagnosed with metastatic breast cancer (MBC) between 2010 and 2018 in the Surveillance, Epidemiology, and End Results (SEER) database were included in this study. All patients were randomly allocated into training and validation groups at a ratio of 7:3. Univariate Cox hazard regression, least absolute shrinkage and selection operator (LASSO), and best subset regression (BSR) were used for initial variable selection, followed by a backward stepwise multivariate Cox regression to identify prognostic factors and construct a nomogram. Following the validation of the nomogram with concordance indexes (C-index), receiver operating characteristic (ROC) curves, calibration curves, and decision curve analyses (DCAs), risk stratifications were established. Results Age, marital status, T stage, N stage, breast subtype, bone metastasis, brain metastasis, liver metastasis, lung metastasis, radiotherapy, and chemotherapy were independent prognostic factors for BCSS. The C-indexes were 0.707 [95% confidence interval (CI), 0.666-0.748] in the training group and 0.702 (95% CI, 0.639-0.765) in the validation group. In the training group, the AUCs for BCSS were 0.857 (95% CI, 0.770-0.943), 0.747 (95% CI, 0.689-0.804), and 0.700 (95% CI, 0.643-0.757) at 1 year, 3 years, and 5 years, respectively, while in the validation group, the AUCs were 0.840 (95% CI, 0.733-0.947), 0.763 (95% CI, 0.677-0.849), and 0.709 (95% CI, 0.623-0.795) for the same time points. The calibration curves for BCSS probability prediction demonstrated excellent consistency. The DCA curves exhibited strong discrimination power and yielded substantial net benefits. Conclusions The nomogram, constructed based on prognostic risk factors, has the ability to provide personalized predictions for BCSS in dnMBC patients undergoing IBR and serve as a valuable reference for clinical decision making.

Keywords: SEER database; breast cancer-specific survival; de novo metastatic breast cancer; immediate breast reconstruction; nomogram.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Flow diagram. SEER: the Surveillance, Epidemiology and End Results database, BCSS: breast cancer-specific survival.
Figure 2
Figure 2
The forest plot of univariate Cox hazard regression in the training cohort. HR—hazard ratio, CI—confidence interval, IDC—invasive ductal cancer, ILC—invasive lobular cancer, HR—hormone receptor, HER2—human epithelial growth factor receptor type 2.
Figure 3
Figure 3
Least absolute shrinkage and selection operator regression (A,B) and best subset regression (C) for initial variable selection. RT—radiotherapy, CT—chemotherapy, HR—hormone receptor, HER2—human epithelial growth factor receptor type 2.
Figure 4
Figure 4
Comparison among different models. (A) Comparison of AUC values for the 1-year ROC curves among Uni-cox, LASSO, and BSR models; (B) comparison of AUC values for the 3-year ROC curves among three models; (C) comparison of AUC values for the 5-year ROC curves among models; (D) comparison of AIC values among models.
Figure 5
Figure 5
Nomogram to predict 1-, 3-, and 5-year of BCSS for de novo metastatic breast cancer patients with immediate breast reconstruction. BCSS—breast cancer-specific survival.
Figure 6
Figure 6
The time-dependent ROC curves of the nomogram predicting (A) 1-year BCSS, (B) 3-year BCSS, and (C) 5-year BCSS of the training and validation groups, respectively. The calibration curves of the nomogram for predicting (D) 1-year BCSS, (E) 3-year BCSS, and (F) 5-year BCSS of the training and validation groups, respectively. The decision curve analysis of the nomogram for predicting (G) 1-year BCSS, (H) 3-year BCSS, and (I) 5-year BCSS of the training and validation groups, respectively.
Figure 6
Figure 6
The time-dependent ROC curves of the nomogram predicting (A) 1-year BCSS, (B) 3-year BCSS, and (C) 5-year BCSS of the training and validation groups, respectively. The calibration curves of the nomogram for predicting (D) 1-year BCSS, (E) 3-year BCSS, and (F) 5-year BCSS of the training and validation groups, respectively. The decision curve analysis of the nomogram for predicting (G) 1-year BCSS, (H) 3-year BCSS, and (I) 5-year BCSS of the training and validation groups, respectively.
Figure 7
Figure 7
Kaplan–Meier curves of BCSS for all patients in the low-, intermediate-, and high-risk groups. BCSS—breast cancer-specific survival.
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