In this study, we hypothesized that the specific genomic profiles of invasive lobular carcinoma (ILC) can be captured with radiomics analysis and machine learning (ML) from standardized dynamic contrast-enhanced breast MRI. Three-dimensional tumor segmentation of the first post-contrast T1-weighted sequence was conducted and included the entire mass and non-mass enhancement lesions, unifocal and multifocal/multicentric lesions. This supervised ML model produced an accuracy of 76.6%, sensitivity of 72.7%, specificity of 80.6%, PPV of 79.1% and NPV of 74.5%. Our preliminary results indicate that radiomics analysis coupled with supervised ML allows a non-invasive differentiation between ILC and invasive ductal carcinoma.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords