Improved characterization of sub-centimeter enhancing breast masses on MRI with radiomics and machine learning in BRCA mutation carriers Journal Article

   

Authors:	Lo Gullo, R.; Daimiel, I.; Rossi Saccarelli, C.; Bitencourt, A.; Gibbs, P.; Fox, M. J.; Thakur, S. B.; Martinez, D. F.; Jochelson, M. S.; Morris, E. A.; Pinker, K.
Article Title:	Improved characterization of sub-centimeter enhancing breast masses on MRI with radiomics and machine learning in BRCA mutation carriers
Abstract:	Objectives: To investigate whether radiomics features extracted from MRI of BRCA-positive patients with sub-centimeter breast masses can be coupled with machine learning to differentiate benign from malignant lesions using model-free parameter maps. Methods: In this retrospective study, BRCA-positive patients who had an MRI from November 2013 to February 2019 that led to a biopsy (BI-RADS 4) or imaging follow-up (BI-RADS 3) for sub-centimeter lesions were included. Two radiologists assessed all lesions independently and in consensus according to BI-RADS. Radiomics features were calculated using open-source CERR software. Univariate analysis and multivariate modeling were performed to identify significant radiomics features and clinical factors to be included in a machine learning model to differentiate malignant from benign lesions. Results: Ninety-six BRCA mutation carriers (mean age at biopsy = 45.5 ± 13.5 years) were included. Consensus BI-RADS classification assessment achieved a diagnostic accuracy of 53.4%, sensitivity of 75% (30/40), specificity of 42.1% (32/76), PPV of 40.5% (30/74), and NPV of 76.2% (32/42). The machine learning model combining five parameters (age, lesion location, GLCM-based correlation from the pre-contrast phase, first-order coefficient of variation from the 1st post-contrast phase, and SZM-based gray level variance from the 1st post-contrast phase) achieved a diagnostic accuracy of 81.5%, sensitivity of 63.2% (24/38), specificity of 91.4% (64/70), PPV of 80.0% (24/30), and NPV of 82.1% (64/78). Conclusions: Radiomics analysis coupled with machine learning improves the diagnostic accuracy of MRI in characterizing sub-centimeter breast masses as benign or malignant compared with qualitative morphological assessment with BI-RADS classification alone in BRCA mutation carriers. Key Points: • Radiomics and machine learning can help differentiate benign from malignant breast masses even if the masses are small and morphological features are benign. • Radiomics and machine learning analysis showed improved diagnostic accuracy, specificity, PPV, and NPV compared with qualitative morphological assessment alone. © 2020, The Author(s).
Keywords:	magnetic resonance imaging; breast neoplasms; artificial intelligence; machine learning
Journal Title:	European Radiology
Volume:	30
Issue:	12
ISSN:	0938-7994
Publisher:	Springer
Date Published:	2020-12-01
Start Page:	6721
End Page:	6731
Language:	English
DOI:	10.1007/s00330-020-06991-7
PUBMED:	32594207
PROVIDER:	scopus
PMCID:	PMC7599163
DOI/URL:	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086929577&doi=10.1007%2fs00330-020-06991-7&partnerID=40&md5=91f4be8fad5cf046bb49a5ebcc5516dd
Notes:	Article -- Export Date: 1 December 2020 -- Source: Scopus

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