Objective:To identify the risk factors influencing the prognosis of patients with advanced breast cancer through LASSO-Logistic regression analysis and construct a nomogram model to evaluate their prognostic risk.Methods:A total of 178 patients with advanced breast cancer who visited the Department of Thyroid and Breast Surgery of Chengyang District People’s Hospital of Qingdao City from Jan. 2015 to Jan. 2023 were selected as the research subjects. According to the follow-up results, the patients were divided into a good-prognosis group and a poor-prognosis group. Clinical data of the patients were collected. LASSO-Logistic regression analysis was used to identify the risk factors affecting the prognosis of patients with advanced breast cancer. A nomogram model was constructed based on the analysis results. The predictive efficacy of the model for the prognostic risk of patients with advanced breast cancer was evaluated using the receiver operating characteristic (ROC) curve and Hosmer-Lemeshow (H-L) test.Results:During the follow-up, 5 patients were lost to follow-up. Among the final 173 patients included, 60 had poor prognoses (accounting for 34.68%), and 113 had good prognoses (accounting for 65.32%). There were significant differences between the poor prognosis group and the good prognosis group in terms of the number of lymph node metastases ( χ 2=18.12), the number of organ metastases ( χ 2=14.28), the difference in ADC before and after treatment ( t=17.35), the difference in SER before and after treatment ( t=9.57), the enhancement of the echo behind the breast after treatment ( χ 2=13.00), and the proportion of increased calcification ( χ 2=8.06) (both P < 0.05). The clinical data with significant differences in the univariate analysis were included in the LASSO regression analysis. Six factors were finally selected: number of lymph node metastases > 5, number of organ metastases > 1, difference in ADC values before and after treatment, difference in SER values before and after treatment, enhanced echo behind the breast, and increased calcification. These six factors selected by LASSO regression were included in the Logistic regression analysis. The results showed that number of organ metastases > 1 ( OR=2.208, 95% CI: 1.153-3.263), small difference in ADC values before and after treatment ( OR=0.448, 95% CI: 0.287-0.608), enhanced echo behind the breast ( OR=2.474, 95% CI: 1.063-3.886), and increased calcification ( OR=3.762, 95% CI: 1.831-5.693) were independent risk factors for poor prognosis in patients with advanced breast cancer (both P<0.05). A nomogram model was constructed based on the analysis results. The ROC curve showed that the area under the curve (AUC) of the model was 0.778. The H-L test results showed that the calibration curve fit well with the ideal curve, with χ 2 = 0.69 and P = 0.273. Conclusion:The nomogram model constructed based on LASSO-Logistic regression analysis has good predictive efficacy for the prognosis of patients with advanced breast cancer.

Objective:To identify the risk factors influencing the prognosis of patients with advanced breast cancer through LASSO-Logistic regression analysis and construct a nomogram model to evaluate their prognostic risk.Methods:A total of 178 patients with advanced breast cancer who visited the Department of Thyroid and Breast Surgery of Chengyang District People’s Hospital of Qingdao City from Jan. 2015 to Jan. 2023 were selected as the research subjects. According to the follow-up results, the patients were divided into a good-prognosis group and a poor-prognosis group. Clinical data of the patients were collected. LASSO-Logistic regression analysis was used to identify the risk factors affecting the prognosis of patients with advanced breast cancer. A nomogram model was constructed based on the analysis results. The predictive efficacy of the model for the prognostic risk of patients with advanced breast cancer was evaluated using the receiver operating characteristic (ROC) curve and Hosmer-Lemeshow (H-L) test.Results:During the follow-up, 5 patients were lost to follow-up. Among the final 173 patients included, 60 had poor prognoses (accounting for 34.68%), and 113 had good prognoses (accounting for 65.32%). There were significant differences between the poor prognosis group and the good prognosis group in terms of the number of lymph node metastases ( χ 2=18.12), the number of organ metastases ( χ 2=14.28), the difference in ADC before and after treatment ( t=17.35), the difference in SER before and after treatment ( t=9.57), the enhancement of the echo behind the breast after treatment ( χ 2=13.00), and the proportion of increased calcification ( χ 2=8.06) (both P < 0.05). The clinical data with significant differences in the univariate analysis were included in the LASSO regression analysis. Six factors were finally selected: number of lymph node metastases > 5, number of organ metastases > 1, difference in ADC values before and after treatment, difference in SER values before and after treatment, enhanced echo behind the breast, and increased calcification. These six factors selected by LASSO regression were included in the Logistic regression analysis. The results showed that number of organ metastases > 1 ( OR=2.208, 95% CI: 1.153-3.263), small difference in ADC values before and after treatment ( OR=0.448, 95% CI: 0.287-0.608), enhanced echo behind the breast ( OR=2.474, 95% CI: 1.063-3.886), and increased calcification ( OR=3.762, 95% CI: 1.831-5.693) were independent risk factors for poor prognosis in patients with advanced breast cancer (both P<0.05). A nomogram model was constructed based on the analysis results. The ROC curve showed that the area under the curve (AUC) of the model was 0.778. The H-L test results showed that the calibration curve fit well with the ideal curve, with χ 2 = 0.69 and P = 0.273. Conclusion:The nomogram model constructed based on LASSO-Logistic regression analysis has good predictive efficacy for the prognosis of patients with advanced breast cancer.