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

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 (X3, boundary; X4, margin; X6, resistive index; X7, S/L ratio; X9, increase of maximum sectional area; and X14, microcalcification) using multivariate analysis. The combined predictive formula for our model was: Z=−5.937 + 1.435X3 + 1.820X4 + 1.760X6 + 2.312X7 + 3.018X9 + 2.494X14. 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.