Breast cancer prognosis has improved greatly in recent years. Consequently, a thorough search for sensitive prognostic factors, able to help clinicians offer appropriate therapy, has become a priority in this area. In this study, we considered all new cases of invasive breast cancer diagnosed in the Province of Modena, Italy, between 1997 and 2007, registered by the Modena Cancer Registry. The principal endpoint of this study was relapse-free survival (RFS). A set of 11 clinic and pathological parameters was investigated. After a median follow-up of 73 months, 494 relapses were recorded. Tumor size, node status, grading, HER2 and estrogen receptor status were retained as independent factors in a multivariate analysis. Using these variables, a prognostic model was devised to identify three groups at different risk. In the training sample, the 5-year RFS rates resulted 96.0%, 82.9% and 63.7% in patients at low, intermediate and high risk, respectively (p < 0.0001). In the validation sample, the 5-year RFS was 96.2%, 85.4% and 66.9%, respectively. To conclude our study demonstrates that a very simple prognostic index based on easily available clinical data may represent a useful tool for the identification of patients at different risk of relapse and may be a notable device to predict who truly benefits from medical treatment.
Tumor size, node status, grading, HER2 and estrogen receptor status still retain a strong value in patients with operable breast cancer diagnosed in recent years.
GUARNERI, VALENTINA;CONTE, PIERFRANCO;
2013
Abstract
Breast cancer prognosis has improved greatly in recent years. Consequently, a thorough search for sensitive prognostic factors, able to help clinicians offer appropriate therapy, has become a priority in this area. In this study, we considered all new cases of invasive breast cancer diagnosed in the Province of Modena, Italy, between 1997 and 2007, registered by the Modena Cancer Registry. The principal endpoint of this study was relapse-free survival (RFS). A set of 11 clinic and pathological parameters was investigated. After a median follow-up of 73 months, 494 relapses were recorded. Tumor size, node status, grading, HER2 and estrogen receptor status were retained as independent factors in a multivariate analysis. Using these variables, a prognostic model was devised to identify three groups at different risk. In the training sample, the 5-year RFS rates resulted 96.0%, 82.9% and 63.7% in patients at low, intermediate and high risk, respectively (p < 0.0001). In the validation sample, the 5-year RFS was 96.2%, 85.4% and 66.9%, respectively. To conclude our study demonstrates that a very simple prognostic index based on easily available clinical data may represent a useful tool for the identification of patients at different risk of relapse and may be a notable device to predict who truly benefits from medical treatment.Pubblicazioni consigliate
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