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A prognostic score in histological node negative breast cancer
B Chevallier1, V Mosseri, J P Dauce
1Service de Médecine Intene et chimiothérapie, Centre H. Becquerel, Rouen, France.
British Journal of Cancer
|March 1, 1990
Summary
This study on breast cancer survival found that age, tumor size, and hormone receptor status significantly impact outcomes. A new prognostic score helps categorize patients into good, intermediate, and poor prognosis groups.
Area of Science:
- Oncology
- Surgical Oncology
- Biomarkers in Cancer
Background:
- Breast cancer treatment involves surgical options like conservative or radical surgery.
- Axillary dissection is a standard procedure in breast cancer surgery.
- Hormone receptor status (Estrogen Receptor and Progesterone Receptor) is crucial for breast cancer prognosis.
Purpose of the Study:
- To evaluate prognostic factors for overall survival (OS) and disease-free survival (DFS) in unilateral, non-metastatic breast cancer patients.
- To develop a prognostic score for stratifying patients based on their survival outlook.
Main Methods:
- Retrospective analysis of 379 breast cancer patients treated between 1977 and 1983.
- Surgical treatment included conservative or radical surgery with axillary dissection.
- Estrogen receptor (ER) and progesterone receptor (PR) levels were measured; Kaplan-Meier and log-rank tests were used for unifactorial and multifactorial analyses.
Main Results:
- At 5 years, overall survival (OS) was 88% and disease-free survival (DFS) was 78%.
- Unifactorial analysis showed age, tumor size, histological grading (SBR), ER, and PR significantly related to OS and DFS.
- Multifactorial analysis identified age, tumor size, histological grading, and PR as significant predictors for DFS and OS.
Conclusions:
- A prognostic score was developed, effectively dividing patients into three distinct prognostic groups (good, intermediate, bad).
- Tumor size and Progesterone Receptor (PR) status are key indicators for overall survival.
- Age, tumor size, and histological grading are critical for predicting disease-free survival.