结合多学科分析的预测模型用于乳腺实性结节恶性风险的临床评估

Prediction model that combines with multidisciplinary analysis for clinical evaluation of malignancy risk of solid breast nodules.

作者信息

Dong Bin, Hu Qiaohong, He Hongfeng, Liu Ying

机构信息

Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China.

Department of ultrasonography, Zhejiang Provincial People's Hospital & People's Hospital of Hangzhou Medical College, Hangzhou, Zhejiang, China.

出版信息

J Int Med Res. 2021 Apr;49(4):3000605211004681. doi: 10.1177/03000605211004681.

DOI:10.1177/03000605211004681

PMID:33845599

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8047088/

Abstract

OBJECTIVE

Few studies have systematically developed predictive models for clinical evaluation of the malignancy risk of solid breast nodules. We performed a retrospective review of female patients who underwent breast surgery or puncture, aiming to establish a predictive model for evaluating the clinical malignancy risk of solid breast nodules.

METHOD

Multivariable logistic regression was used to identify independent variables and establish a predictive model based on a model group (207 nodules). The regression model was further validated using a validation group (112 nodules).

RESULTS

We identified six independent risk factors (X, boundary; X, margin; X, resistive index; X, S/L ratio; X, increase of maximum sectional area; and X, microcalcification) using multivariate analysis. The combined predictive formula for our model was: Z=-5.937 + 1.435X + 1.820X + 1.760X + 2.312X + 3.018X + 2.494X. The accuracy, sensitivity, specificity, missed diagnosis rate, misdiagnosis rate, negative likelihood ratio, and positive likelihood ratio of the model were 88.39%, 90.00%, 87.80%, 10.00%, 12.20%, 7.38, and 0.11, respectively.

CONCLUSION

This predictive model is simple, practical, and effective for evaluation of the malignancy risk of solid breast nodules in clinical settings.

摘要

目的

很少有研究系统地开发用于临床评估实性乳腺结节恶性风险的预测模型。我们对接受乳腺手术或穿刺的女性患者进行了回顾性研究，旨在建立一个评估实性乳腺结节临床恶性风险的预测模型。

方法

采用多变量逻辑回归来识别自变量，并基于一个模型组（207个结节）建立预测模型。使用一个验证组（112个结节）对回归模型进行进一步验证。

结果

通过多变量分析，我们确定了六个独立的风险因素（X，边界；X，边缘；X，阻力指数；X，S/L比值；X，最大截面积增加；以及X，微钙化）。我们模型的联合预测公式为：Z = -5.937 + 1.435X + 1.820X + 1.760X + 2.312X + 3.018X + 2.494X。该模型的准确率、灵敏度、特异度、漏诊率、误诊率、阴性似然比和阳性似然比分别为88.39%、90.00%、87.80%、10.00%、12.20%、7.38和0.11。