Abstract：
OBJECTIVE: To 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.
METHODS: Imaging 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.
RESULTS: Size, 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) in evaluation of breast lesions (all P < 0.05).
CONCLUSIONS: The 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.
METHODS: Imaging 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.
RESULTS: Size, 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) in evaluation of breast lesions (all P < 0.05).
CONCLUSIONS: The 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.
摘要：
目的：评估将常规超声（CUS）与两种不同的应变成像（SI）技术相结合的决策树诊断乳腺病变的有效性，并分析决策树结果中导致假阴性(FN)和假阳性(FP)的因素。
方法：前瞻性收集796例训练组和351例验证组的影像学和临床资料。构建了结合SI和CUS两种类型的决策树模型，并对其诊断性能进行了分析。应用单因素分析和多因素分析来识别与决策树模型的FP和FN结果相关的独立危险因素。
结果：大小，形状,margin,血管，内部钙化的类型，发现EI评分和VTI模式与乳腺良恶性病变的诊断显着独立相关。因此,尺寸,形状,margin,血管，利用EI评分和VTI模式构建决策树模型。树(EI+VTI)模型具有最高的AUC。在培训和验证组中，树的AUC(EI+VTI)明显高于EI,VTI,和BI-RADS（所有，P<0.05)。方向,在评估乳腺病变时，后部声学特征和内部钙化类型与Tree（EIVTI）的误诊结果呈正相关（均P<0.05）。
结论：基于决策树的诊断模型将两种不同类型的SI与CUS集成在一起，可以提高每种方法单独使用时的诊断准确性。这种整合降低了误诊率，可能协助放射科医生进行更有效的病变评估。应用决策树模型时，应该注意方向，后部声学特征，以及病变内部钙化的类型。
方法：前瞻性收集796例训练组和351例验证组的影像学和临床资料。构建了结合SI和CUS两种类型的决策树模型，并对其诊断性能进行了分析。应用单因素分析和多因素分析来识别与决策树模型的FP和FN结果相关的独立危险因素。
结果：大小，形状,margin,血管，内部钙化的类型，发现EI评分和VTI模式与乳腺良恶性病变的诊断显着独立相关。因此,尺寸,形状,margin,血管，利用EI评分和VTI模式构建决策树模型。树(EI+VTI)模型具有最高的AUC。在培训和验证组中，树的AUC(EI+VTI)明显高于EI,VTI,和BI-RADS（所有，P<0.05)。方向,在评估乳腺病变时，后部声学特征和内部钙化类型与Tree（EIVTI）的误诊结果呈正相关（均P<0.05）。
结论：基于决策树的诊断模型将两种不同类型的SI与CUS集成在一起，可以提高每种方法单独使用时的诊断准确性。这种整合降低了误诊率，可能协助放射科医生进行更有效的病变评估。应用决策树模型时，应该注意方向，后部声学特征，以及病变内部钙化的类型。
