Objective:

To establish the ultrasonic decision tree model for predicting different subtypes of invasive breast cancer

and to analyze the clinical value of the model.

Methods:

A total of 420 invasive breast cancer patients were analyzed retrospectively

which were confirmed by pathology. All patients were divided

into 4 molecular subtypes: Luminal A (LA) type (

n

=137)

Luminal B (LB) type (

n

=157)

human epidermal growth factor receptor 2 over-expression (HER2

+

) type (

n

=61) and triple-negative breast cancer (TNBC) type (

n

=65). The ultrasonic and clinical features of patients were evaluated by analysis of variance and Fisher exact probability test

and the statistically significant features were included in the decision tree model for molecular subtype identification of breast cancer. The corresponding area under the receiver operating characteristic (ROC) curves (AUC) were calculated to assess the performance of each decision tree.

Results:

Seven features

including clinical stage

maximum diameter

echo patterns

posterior acoustic features

calcification pattern

the position of calcification

and lymph node metastasis

were statistically significantdifferences (

P

＜0.05) among four molecular subtypes. The AUC of LA

LB

HER2

+

and TNBC type decision tree models were 0.721

0.708

0.722

0.877 respectively in training sets. Moreover

the sensitivity of the decision tree model (81.0%) for TNBC type was higher than that of the senior sonographer.

Conclusion:

The decision tree built by ultrasonic and clinical features has favorable value in the prediction of breast cancer molecular subtypes with high diagnostic accuracy.