Associated factors leading to misdiagnosis of a combined diagnostic model of different types of strain imaging and conventional ultrasound in evaluation of breast lesions: Selection strategy for using different types of strain imaging in evaluation of breast lesions

Abstract

ObjectiveTo evaluate the effectiveness of a decision tree that integrates conventional ultrasound (CUS) with two different strain imaging (SI) techniques for diagnosing breast lesions, and to analyze the factors contributing to false negative (FN) and false positive (FP) in the decision tree’s outcomes. Materials and MethodsImaging and clinical data of 796 cases in the training set and 351 cases in the validation set were prospectively collected. A decision tree model that combines two types of SI and CUS was constructed, and its diagnostic performance was analyzed. Univariate analysis and multivariate analysis were applied to identify independent risk factors associated with FP and FN results of the decision tree model. ResultsSize, shape, margin, vascularity, the types of internal calcifications, EI score and VTI pattern were found to be significantly independently associated with the diagnosis of benign and malignant breast lesions. Therefore, size, shape, margin, vascularity, EI score and VTI pattern were used to construct decision tree models. The Tree (EI+VTI) model had the highest AUC. Both in the training and validation groups, the AUC of Tree (EI+VTI) was significantly higher compared with that of EI, VTI, and BI-RADS (all, P < 0.05). Orientation, posterior acoustic features and the types of internal calcifications were significantly positively associated with misdiagnosis results of Tree (EI+VTI) breast lesions (all P < 0.05). ConclusionThe diagnostic model based on a decision tree that integrates two distinct types of SI with CUS enhances the diagnostic accuracy of each method when used individually. This integration lowers the misdiagnosis rate, potentially assisting radiologists in more effective lesion assessments. When applying the decision tree model, attention should be paid to the orientation, posterior acoustic features, and the types of internal calcifications of the lesions.