Diagnosis of benign and malignant nodules with a radiomics model integrating features from nodules and mammary regions on DCE-MRI

Abstract

Objectives: To establish a radiomics model for distinguishing between the benign and malignant mammary gland nodules via combining the features from nodule and mammary regions on DCE-MRI.

Methods: In this retrospective study, a total of 103 cases with mammary gland nodules (malignant/benign = 80/23) underwent DCE-MRI, and was confirmed by biopsy pathology. Features were extracted from both nodule region and mammary region on DCE-MRI. Three SVM classifiers were built for diagnosis of benign and malignant nodules as follows: the model with the features only from nodule region (N model), with the features only from mammary region (M model) and the model combining the features from nodule region and mammary region (NM model). The performance of models was evaluated with the area under the curve of receiver operating characteristic (AUC).

Results: One radiomic features is selected from nodule region and 3 radiomic features is selected from mammary region. Compared with N or M model, NM model exhibited the best performance with an AUC of 0.756.

Conclusions: Compared with the model only using the features from nodule or mammary region, the radiomics-based model combining the features from nodule and mammary region outperformed in the diagnosis of benign and malignant nodules.

Keywords: DCE-MRI; breast cancer; mammary region; nodule region; radiomics.

Figure 1

The illustration of two ROIs. Left column: nodule region (N-ROI), right column: mammary region (M-ROI), the first row: benign, the second row: malignant.

Figure 2

The workflow of radiomics analysis.

Figure 3

ROC of three models, (A) the model with the features only from nodule region (N model), (B) the model with the features only from mammary region (M model), (C) the model combining the features from nodule region and mammary region (NM model).