Survival Analysis in Male Breast Cancer With Bone Metastasis Based on the SEER Database

Development and Validation of a Prognostic Nomogram Model for HER2-Positive Male Breast Cancer Patients

BACKGROUND

HER2-positive male breast cancer (MBC) is a rare condition that has a poor prognosis. The purpose of this study was to establish a nomogram model for predicting the prognosis of HER2-positive MBC patients.

METHODS

240 HER2-positive MBC patients from 2004 to 2015 were retrieved from the surveillance, epidemiology, and end results (SEER) database. All HER2-positive MBC patients were divided randomly into training (n = 144) and validation cohorts (n = 96) according to a ratio of 6:4. Univariate and multivariate Cox regression analyses were used to determine the prognostic factors associated with HER2-positive MBC patients. A clinical prediction model was constructed to predict the overall survival of these patients. The nomogram model was assessed by using receiver operating characteristics (ROC) curves, calibration plots and decision curve analysis (DCA).

RESULTS

The Cox regression analysis showed that T-stage, M-stage, surgery and chemotherapy were independent risk factors for the prognosis of HER2-positive MBC patients. The model could also accurately predict the Overall survival (OS) of the patients. In the training and validation cohorts, the C indexes of the OS nomograms were 0.746 (0.677-0.815) and 0.754 (0.679-0.829), respectively. Calibration curves and DCA verified the reliability and accuracy of the clinical prediction model.

CONCLUSION

In conclusion, the predictive model constructed had good clinical utility and can help the clinician to select appropriate treatment strategies for HER2-positive MBC patients.

Risk stratification system and visualized dynamic nomogram constructed for predicting diagnosis and prognosis in rare male breast cancer patients with bone metastases

Background

Bone metastases (BM) from malignant tumors could disrupt the balance between osteoclasts and osteoblasts and affect bone homeostasis. Malignant breast cancer (BC) is rare in male patients, and co-occurrence of BM is even rarer. Given its low incidence, there is limited research evaluating risk and prognosis. Despite the widespread application of nomograms to predict uncommon malignancies, no studies have constructed predictive models focusing on the diagnosis and prognosis of male breast cancer with bone metastases (MBCBM).

Methods

This study selected all male breast cancer patients (MBC) between 2010 and 2019 in the Surveillance, Epidemiology, and End Results (SEER) database. We used simple and multivariate Logistic regression analyses to identify independent risk factors for BM in MBC patients. Then simple and multivariate Cox regression analyses were employed to determine the independent prognostic factors for overall survival (OS) and cancer-specific survival (CSS) in MBCBM patients. We established and validated three new nomograms based on these independent factors.

Result

A total of 4187 MBC patients were included, with 191 (4.56%) having bone metastases at the time of diagnosis. The independent risk factors of BM in MBC patients included age, tumor size, marital status, T stage, and N stage. In MBCBM patients, independent prognostic factors for OS and CSS were both age, T stage, ER status, PR status, and surgery. The concordance index (C-index), the area under the curve (AUC) of the receiver operating characteristic curve (ROC), the calibration curve, and the decision curve analysis (DCA) confirmed that these three nomograms could accurately predict the diagnosis and prognosis of MBCBM patients with excellent discrimination and clinical utility superior to the TNM staging system. We then established two prognostic-based risk stratification systems and three visualized dynamic nomograms that could be applied in clinical practice.

Conclusion

In conclusion, this study aimed to establish and validate an accurate novel nomogram to objectively predict the diagnosis and prognosis of MBCBM patients. On this basis, prognostic-based risk stratification systems and visualized dynamic nomograms were constructed to facilitate doctors and patients to quantify individual BM risk probability and survival probability to assist in personalized risk assessment and clinical decision-making.