Breast MRI radiomics: comparison of computer- and human-extracted imaging phenotypes

Elizabeth J Sutton; Erich P Huang; Karen Drukker; Elizabeth S Burnside; Hui Li; Jose M Net; Arvind Rao; Gary J Whitman; Margarita Zuley; Marie Ganott; Ermelinda Bonaccio; Maryellen L Giger; Elizabeth A Morris; TCGA group

doi:10.1186/s41747-017-0025-2

Breast MRI radiomics: comparison of computer- and human-extracted imaging phenotypes

Eur Radiol Exp. 2017;1(1):22. doi: 10.1186/s41747-017-0025-2. Epub 2017 Nov 21.

Authors

Affiliations

¹ 1Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065 USA.
² 2Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, 9609 Medical Center Drive, Rockville, MD 20892 USA.
³ 3Department of Radiology, University of Chicago, 5841 South Maryland Avenue, MC 2026, Chicago, IL 60637 USA.
⁴ 4Department of Radiology, University of Wisconsin School of Medicine and Public Health, 600 Highland Avenue, Madison, WI 53792 USA.
⁵ 5Miller School of Medicine, University of Miami, Miami, FL 33136 USA.
⁶ 6Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77498 USA.
⁷ 7Department of Diagnostic Imaging, The University of Texas MD Anderson Cancer, Center, Houston, TX 77030 USA.
⁸ 8Department of Radiology, University of Pittsburgh, Pittsburgh, PA 15213 USA.
⁹ 9Department of Radiology, Roswell Park Cancer Institute, Buffalo, NY 14263 USA.
¹⁰ 300 East 66th Street, New York, NY 10065 USA.

Abstract

Background: In this study, we sought to investigate if computer-extracted magnetic resonance imaging (MRI) phenotypes of breast cancer could replicate human-extracted size and Breast Imaging-Reporting and Data System (BI-RADS) imaging phenotypes using MRI data from The Cancer Genome Atlas (TCGA) project of the National Cancer Institute.

Methods: Our retrospective interpretation study involved analysis of Health Insurance Portability and Accountability Act-compliant breast MRI data from The Cancer Imaging Archive, an open-source database from the TCGA project. This study was exempt from institutional review board approval at Memorial Sloan Kettering Cancer Center and the need for informed consent was waived. Ninety-one pre-operative breast MRIs with verified invasive breast cancers were analysed. Three fellowship-trained breast radiologists evaluated the index cancer in each case according to size and the BI-RADS lexicon for shape, margin, and enhancement (human-extracted image phenotypes [HEIP]). Human inter-observer agreement was analysed by the intra-class correlation coefficient (ICC) for size and Krippendorff's α for other measurements. Quantitative MRI radiomics of computerised three-dimensional segmentations of each cancer generated computer-extracted image phenotypes (CEIP). Spearman's rank correlation coefficients were used to compare HEIP and CEIP.

Results: Inter-observer agreement for HEIP varied, with the highest agreement seen for size (ICC 0.679) and shape (ICC 0.527). The computer-extracted maximum linear size replicated the human measurement with p < 10^-12. CEIP of shape, specifically sphericity and irregularity, replicated HEIP with both p values < 0.001. CEIP did not demonstrate agreement with HEIP of tumour margin or internal enhancement.

Conclusions: Quantitative radiomics of breast cancer may replicate human-extracted tumour size and BI-RADS imaging phenotypes, thus enabling precision medicine.

Keywords: Breast cancer; Inter-observer variability; Machine learning; Magnetic resonance imaging; Radiomics.

Abstract

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