Objective:

To investigate the value of ultrasound-based peri-tumoral radiomics in discriminating benign and malignant breast nodules.

Methods:

A total of 300 cases of breast masses patients who were screened by regular ultrasound examination in The Sixth People’s Hospital

Shanghai Jiao Tong University School of Medicine were retrospectively collected. For、 the lesion on the regular ultrasound image

the largest dimension was selected to outline the region of interest. Subsequently

this area was automatically expanded by 2 mm in all directions

conformally and outwardly

to extract intra- and peritumor radiomics features. The included cases were randomly divided into a training group (210 cases) and a validation group (90 cases) in a ratio of 7∶3. Apply the least absolute shrinkage and selection operator (LASSO) to perform feature selection and obtain the optimal featurecombination. The optimal features of the included models were retained by dimensionality reduction of the imaging omics features. The support vector machine (SVM) model was used to classify benign and malignant breast nodules

establish the intra-tumoral

peritumoral

clinical variables

intra-tumoral + peritumoral

intra-tumoral + peritumoral + clinical variables respectively

and evaluate the diagnostic efficacy of ultrasonic breast nodules by the receiver operating characteristics (ROC) curve.

Results:

Among 300 breast nodules

101 were malignant nodules and 199 were benign nodules. The ultrasound-based intra-tumoral radiomics model had an area under curve (AUC) value of 0.927 (95% CI 0.889-0.965) in the training group and 0.808 (95% CI 0.710-0.905) in the validation group. The accuracy

sensitivity

specificity

F1 value

and precision were 0.753

0.731

0

.763

0.644

and 0.576 in the validation group

respectively in the ultrasound-based intra-tumoral radiomics model. The ultrasound-based peri-tumoral radiomics model had an AUC value of 0.930 (95% CI 0.891-0.969) in the training group and 0.857 (95% CI 0.763-0.949) in the validation group

and the accuracy

sensitivity

specificity

F1 value

and precision of this model were 0.812、0.846、0.797、0.733、0.647 for the validation group

respectively in the ultrasound-based peri-tumoral radiomics model. The intratumorally combined with peritumoral ultrasound imaging histological features had an AUC value of 0.941 (95% CI 0.843-0.967) in the training group and 0.865 (95% CI 0.781-0.949) in the validation group

the accuracy

sensitivity

specificity

F1 value

and precision of the model were 0.824

0.692

0.881

0.706

0.720 in the validation group

respectively. The model with intra-perineural radiomics features combined with clinical variables had an AUC value of 0.952 (95% CI 0.924-0.979) in the training set and an AUC value of 0.873 (95% CI: 0.788 to 0.958) in the validation group

and the accuracy

sensitivity

specificity

F1 value

and precision of the validation group were 0.859

0.692

0.932

0.750

and 0.818

respectively. The diagnostic efficacy of the intra-peri-tumoral combined with clinical variables model was better than that of the clinical variables group and intratumoral imaging histology

with statistically significant differences (

P

＜0.05); it was higher than that of the peri-tumoral and intratumoral combined with peri-tumoral models

but the differences were not statistically significant (

P

＞0.05).

Conclusion:

Both intra-tumoral and peri-tumoral ultrasound radiomics have high value in the diagnosis of benign and malignant breast nodules. The application of intra-tumoral and peri-tumoral radiomics can reduce the missed rate of breast cancer and unnecessary biopsies.