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Poster Display session

50P - Establishment and validation of a predictive model for the risk and prognosis of bone metastasis in breast cancer: Research based on provincial hospital data in China

Date

07 Dec 2024

Session

Poster Display session

Presenters

Lin Miao

Citation

Annals of Oncology (2024) 35 (suppl_4): S1418-S1425. 10.1016/annonc/annonc1685

Authors

L. Miao, X.F. Zhang

Author affiliations

  • Breast Surgery, Liaoning Cancer Hospital & Institute, 110042 - Shenyang/CN

Resources

This content is available to ESMO members and event participants.

Abstract 50P

Background

Bone metastases affect 80% of patients with advanced breast cancer(BC), resulting in several skeletal-related events, which results in significantly reduced overall survival and poorer quality of life for patients. This study retrospectively analyzed the clinical features of breast cancer bone metastasis(BCBM) patients and the risk factors related to the incidence and prognosis of bone metastasis(BM), and established and validated the prediction model of risk and prognosis of BM.

Methods

Clinical characteristics of 2617 patients with BC treated in Liaoning Cancer Hospital from January 2018 to December 2020 were collected, including 629 BCBM patients and 1988 patients without BM. All patients were randomly divided into a training group and a verification group, including 1755 in the training set and 862 in the verification set. A stepwise LR regression analysis method was used to screen out the predictors of BCBM prediction model and nomogram model was constructed.

Results

The results of multivariate logistic regression analysis showed that T stage, N stage, tissue type, menstrual status, lymphocyte, hemoglobin, alkaline phosphatase and CA153 were independent risk factors for BCBM (P<0.05). The AUC (95%CI) in the training set and validation set of the nomogram constructed by the selected risk factors were 0.930 and 0.938 respectively. Through Hosmer-Lemeshow test on the prediction model, Bootstrap sampling conducted 1000 internal calibration on the nomogram, which showed that the calibration curves of the training set and the verification set were close to the ideal 45°reference line. The analysis of decision curve of training set and validation set in this study shows that it has strong clinical practicability in a certain threshold probability range.

Conclusions

The model is a practical tool for predicting BM in BC patients, which can provide an effective decision-making basis for clinicians and a relatively reliable clinical preventive treatment plan to a certain extent.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

The authors.

Funding

Has not received any funding.

Disclosure

All authors have declared no conflicts of interest.

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