Purpose: Bone metastasis in breast cancer remains globally concerned. Accurate survival estimation would be beneficial for clinical decision-making, especially for the patients with potential indications of surgery. Based on a retrospective cohort from China, the study aimed to construct a prognostic prediction nomogram for breast cancer patients with bone metastasis.

Methods: Breast cancer patients with bone metastasis diagnosed between 2009 and 2017 in our department were retrospectively selected. The total cohort was divided into construction and validation cohorts (ratio 7 : 3). A nomogram was constructed to predict the probability of survival, and the performance of model was validated.

Results: A total of 343 patients were enrolled with 243 and 100 patients in construction and validation cohorts, respectively. The median overall survival for the total cohort was 63.2 (95% CI: 52.4-74.0) months. Elevated ALP (HR = 1.71, 95% CI: 1.16-2.51; =0.006), no surgery for breast cancer (HR = 2.19, 95% CI: 1.30-3.70; =0.003), synchronous bone metastasis (HR = 1.98, 95% CI: 1.22-3.22; =0.006), and liver metastasis (HR = 1.68, 95% CI: 1.20-2.37; =0.003) were independent prognostic factors for worse survival. The independent predictors and other five factors (including age at diagnosis, ER status, PR status, Her-2 status, and the performance of bisphosphonate) were incorporated to construct the nomogram. The C-index was 0.714 (95% CI: 0.636-0.792) and 0.705 (95% CI: 0.705) in the construction cohort and validation cohort, respectively. All the calibration curves were close to the 45-degree line, which indicated satisfactory calibration.

Conclusion: A retrospective study aiming at prognostic estimation of breast cancer patients with bone metastasis was designed. Four independent prognostic factors were identified and a prognostic nomogram was constructed with satisfactory discrimination and calibration. The model could be used in survival estimation and individualized treatment planning.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9187277PMC
http://dx.doi.org/10.1155/2022/7140884DOI Listing

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