Breast Lesion Classification with Multiparametric Breast MRI Using Radiomics and Machine Learning: A Comparison with Radiologists' Performance

Abstract

This multicenter retrospective study compared the performance of radiomics analysis coupled with machine learning (ML) with that of radiologists for the classification of breast tumors. A total of 93 consecutive women (mean age: 49 ± 12 years) with 104 histopathologically verified enhancing lesions (mean size: 22.8 ± 15.1 mm), classified as suspicious on multiparametric breast MRIs were included. Two experienced breast radiologists assessed all of the lesions, assigning a Breast Imaging Reporting and Database System (BI-RADS) suspicion category, providing a diffusion-weighted imaging (DWI) score based on lesion signal intensity, and determining the apparent diffusion coefficient (ADC). Ten predictive models for breast lesion discrimination were generated using radiomic features extracted from the multiparametric MRI. The area under the receiver operating curve (AUC) and the accuracy were compared using McNemar's test. Multiparametric radiomics with DWI score and BI-RADS (accuracy = 88.5%; AUC = 0.93) and multiparametric radiomics with ADC values and BI-RADS (accuracy= 88.5%; AUC = 0.96) models showed significant improvements in diagnostic accuracy compared to the multiparametric radiomics (DWI + DCE data) model (p = 0.01 and p = 0.02, respectively), but performed similarly compared to the multiparametric assessment by radiologists (accuracy = 85.6%; AUC = 0.03; p = 0.39). In conclusion, radiomics analysis coupled with the ML of multiparametric MRI could assist in breast lesion discrimination, especially for less experienced readers of breast MRIs.

Keywords: breast neoplasms; diffusion magnetic resonance imaging; machine learning; magnetic resonance imaging; multiparametric magnetic resonance imaging.

Figure 1

Flowchart for the selection of patients in the study. DWI, diffusion-weighted imaging.

Figure 2

Axial MR images of a 48-year-old woman with a 14-mm benign mass in the right breast, of which biopsy yielded fibro-adenomatoid changes (yellow arrows). (A) Axial dynamic contrast-enhanced image depicts a heterogeneous, oval, and circumscribed enhancing mass in the right breast corresponding to a heterogeneous hyperintense lesion on axial diffusion-weighted imaging (DWI) at a b value of 800 s/mm²; (B). (C) Correlative parametric apparent diffusion coefficient (ADC) map with a region of interest (ROI) placed within the darkest part of the lesion and ROI information. ADC values are expressed in mm²/s. This lesion was heterogeneous vs. non-enhancing septa and therefore characterized as BI-RADS 3 and 3 based on the DWI score in the consensus reading of radiologists.

Figure 3

Workflow for the radiomics analysis. DCE-MRI, dynamic contrast-enhanced MRI; DWI, diffusion-weighted imaging; CERR, Computational Environment for Radiological Research; LASSO, least absolute shrinkage and selection operator; SVM, support vector machine.