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

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.