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Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
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Predicting Overall Survival in Patients with Male Breast Cancer: Nomogram Development and External Validation Study.
Wen-Zhen Tang1, Shu-Tian Mo1, Yuan-Xi Xie2
1Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
JMIR Cancer
|March 4, 2025
Summary
This study developed a nomogram to predict overall survival in male breast cancer (MBC) patients. The model accurately identifies risk subgroups, improving clinical decision-making for this rare disease.
Area of Science:
- Oncology
- Cancer Prognostics
- Epidemiology
Background:
- Male breast cancer (MBC) is a rare malignancy with limited prognostic studies.
- Understanding MBC prognosis is crucial due to its infrequent occurrence.
Purpose of the Study:
- To develop and externally validate a nomogram for predicting overall survival in male breast cancer patients.
- To provide a tool for improved clinical decision-making in MBC management.
Main Methods:
- Utilized the Surveillance, Epidemiology, and End Results (SEER) database for model development and internal validation.
- Employed Cox regression analysis to identify significant prognostic variables.
- Externally validated the nomogram using data from a Chinese hospital cohort.
Main Results:
- A nomogram incorporating 7 variables (age, surgery, marital status, tumor stage, clinical stage, chemotherapy, HER2 status) was constructed.
- The nomogram demonstrated high accuracy with concordance indices of 0.72 (training), 0.747 (internal validation), and 0.981 (external validation).
- The model effectively differentiated risk subgroups, showing significantly better survival for low-risk MBC patients.
Conclusions:
- A validated nomogram for male breast cancer survival prediction has been developed.
- This tool offers a scientific basis for clinical diagnosis and treatment strategies in MBC.
- The nomogram aids in stratifying patients and guiding personalized care for improved outcomes.
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