Prediction of breast cancer molecular subtypes using radiomics signatures of synthetic mammography from digital breast tomosynthesis

Abstract

We aimed to predict molecular subtypes of breast cancer using radiomics signatures extracted from synthetic mammography reconstructed from digital breast tomosynthesis (DBT). A total of 365 patients with invasive breast cancer with three different molecular subtypes (luminal A + B, luminal; HER2-positive, HER2; triple-negative, TN) were assigned to the training set and temporally independent validation cohort. A total of 129 radiomics features were extracted from synthetic mammograms. The radiomics signature was built using the elastic-net approach. Clinical features included patient age, lesion size and image features assessed by radiologists. In the validation cohort, the radiomics signature yielded an AUC of 0.838, 0.556, and 0.645 for the TN, HER2 and luminal subtypes, respectively. In a multivariate analysis, the radiomics signature was the only independent predictor of the molecular subtype. The combination of the radiomics signature and clinical features showed significantly higher AUC values than clinical features only for distinguishing the TN subtype. In conclusion, the radiomics signature showed high performance for distinguishing TN breast cancer. Radiomics signatures may serve as biomarkers for TN breast cancer and may help to determine the direction of treatment for these patients.

Figure 1

Patient selection.

Figure 2

Segmentation example 1. Example of tumor segmentation on synthetic mammography. The synthetic mediolateral oblique (A) and craniocaudal (B) views of a 58-year-old female diagnosed with the triple negative subtype of breast cancer. The breast lesion appears as a circumscribed and round mass with high density (arrow).

Figure 3

Segmentation example 2. Example of tumor segmentation on synthetic mammography. The synthetic mediolateral oblique (A) and craniocaudal (B) views of a 47-year-old female diagnosed with the luminal subtype of breast cancer. The breast lesion appears as a spiculated mass with architectural distortion (arrow).

Figure 4

The ROC curve, calibration curve and decision curve of clinical and combined models for distinguishing TN vs. non-TN in the validation cohort. (A) ROC curve of the clinical model (blue dotted line) and combined model (red solid line). The AUC of the combined model was 0.868 and that of the clinical model was 0.665. The two ROC curves showed significant difference (p = 0.0449). (B) Calibration curves of clinical and combined models. The 45◦ black dotted line expresses the ideal prediction. The combined model is closer to the ideal prediction compared to the clinical model, especially at predicted probability of 0.3 or higher. (C) Decision curve of clinical and combined models. In the interval between 5 and 71% of threshold probability, the combined model adds more benefit than applying all or none of the patients, and clinical model.

Figure 5

Correlation between the radiomics signature and BI-RADS features for the (A) TN, (B) HER2 and (C) luminal subtype.