Abstract
Patients with de novo metastatic breast cancer (MBC) constitute a heterogeneous group with different clinicopathologic characteristics and survival outcomes. Despite controversy regarding its prognostic value, primary tumor surgery may improve survival for selected patients. Patients with de novo MBC were identified using the SEER database and were then divided randomly into training and validation sets. A Fine-Gray competing risks model was developed to identify the variables associated with increased cancer-specific mortality in the training set. The M1 subdivision system was established based on the independent prognostic factors. Cumulative incidence curves were estimated and compared using Gray's test. Involvement of brain or liver and number of metastatic sites were identified as independent prognostic factors in multivariate analysis. The M1 category was subdivided into 3 subcategories: M1a, single site involvement except brain and liver; M1b, liver involvement only, or multiple site involvement except brain and liver; and M1c, brain involvement regardless of number of metastatic sites, or liver and other sites involvement except brain (M1b vs M1a: subdistribution hazard ratio [SHR], 1.48; 95% CI, 1.29-1.68; M1c vs M1a: SHR, 2.45; 95% CI, 2.18-2.75). Patients with the M1a subtype benefited most from primary tumor surgery in the adjusted competing risks model (M1a: SHR, 0.57; 95% CI, 0.48-0.67, M1b: SHR, 0.62; 95% CI, 0.47-0.83, and M1c: SHR, 0.59; 95% CI, 0.44-0.80), whereas benefits conferred by treatment with chemotherapy alone increased with the upstaging of metastatic disease (M1a: SHR, 0.72; 95% CI, 0.62-0.83, M1b: SHR, 0.54; 95% CI, 0.44-0.68, and M1c: SHR, 0.53; 95% CI, 0.45-0.61). Subdivision of M1 stage facilitates prognosis prediction and treatment planning for patients with de novo MBC. Treatment offered should be decided in a coordinated multidisciplinary setting. Primary tumor surgery may play an important role in the management of selected patients.
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More From: Journal of the National Comprehensive Cancer Network
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