摘要
Abstract
Objective To construct a risk prediction model for distant metastasis in postoperative triple-negative breast cancer patients based on quantitative parameters of elastography combined with preoperative Ki67.Methods A total of 200 patients with triple-negative breast cancer who were diagnosed in Taian Cancer Hospital from May 2021 to May 2023 were selected as research objects.According to the follow-up results,they were divided into distant metastasis group and non-distant metastasis group.The logistic analysis was used to analyze the influencing factors affecting distant metastasis and construct a risk prediction model.Results After follow-up,65 patients developed distant metastasis,with a metastasis rate of 32.5%.Univariate analysis showed that the percentage of preoperative Ki67≥20%,the percentage of stage Ⅱ,carcinoembryonic antigen,carcinoembryonic antigen 15-3,elasticity score ratio(ESR),lateral strain ratio(LSR),and longitudinal flexural load ratio(LFLR)were higher in the distant metastases group than those in the non-distant metastasis group,and the differences were statistically significant(P<0.05).Multivariate analysis showed that preoperative Ki67,ESR,LSR,and LFLR were independent risk factors for distant metastasis in patients(P<0.05).A column chart was established based on four factors including preoperative Ki67,ESR,LSR,and LFLR,and the area under the curve of the prediction model was 0.90(95%CI:0.86-0.94).The P value of Hosmer-Lemeshow test was 1.000,indicating good predictive performance of the model.The decision curve analysis showed that the model exhibited significant net clinical benefits in modeling.Conclusion The risk assessment model established by quantitative parameters of elastography combined with preoperative Ki67 has high evaluation value and can be used to evaluate the distant metastasis in patients with triple-negative breast cancer after surgery.关键词
三阴性乳腺癌/弹性成像定量参数/Ki67/远处转移/风险模型Key words
Triple-negative breast cancer/Quantitative parameters of elastography/Ki67/Distant metastasis/Risk prediction model分类
医药卫生
