TY - JOUR
T1 - Breast MRI radiomics
T2 - comparison of computer- and human-extracted imaging phenotypes
AU - on behalf of the TCGA group
AU - Sutton, Elizabeth J.
AU - Huang, Erich P.
AU - Drukker, Karen
AU - Burnside, Elizabeth S.
AU - Li, Hui
AU - Net, Jose M.
AU - Rao, Arvind
AU - Whitman, Gary J.
AU - Zuley, Margarita
AU - Ganott, Marie
AU - Bonaccio, Ermelinda
AU - Giger, Maryellen L.
AU - Morris, Elizabeth A.
N1 - Funding Information:
EJS and EAM were supported in part through National Institutes of Health (NIH)/National Cancer Institute (NCI) Cancer Center Support grant P30 CA008748. KD, HL and MLG received support from NIH/NCI grant U01CA195564. The sponsor had no involvement in the design of the study; in the collection, analysis or interpretation of data; or in the writing of the manuscript.
Funding Information:
EJS and EAM were supported in part through National Institutes of Health (NIH)/National Cancer Institute (NCI) Cancer Center Support grant P30 CA008748. KD, HL and MLG received support from NIH/NCI grant U01CA195564. The sponsor had no involvement in the design of the study; in the collection, analysis or interpretation of data; or in the writing of the manuscript. In our present study, we retrieved data from an open-source de-identified database, The Cancer Imaging Archive (TCIA), which is the imaging counterpart of The Cancer Genome Archive (TCGA). Clinical, pathologic and genomic data were extracted using TCGA assembler, an open-source, publicly available, free tool (http://www.nature.com/nmeth/journal/v11/n6/full/nmeth.2956.html). All breast MRI data were downloaded from the Breast Cancer Risk Assessment collection within TCIA (http://www.cancerimagingarchive.net/).
Publisher Copyright:
© 2017, The Author(s).
PY - 2017/12/1
Y1 - 2017/12/1
N2 - 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.
AB - 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.
KW - Breast cancer
KW - Inter-observer variability
KW - Machine learning
KW - Magnetic resonance imaging
KW - Radiomics
UR - http://www.scopus.com/inward/record.url?scp=85050193438&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85050193438&partnerID=8YFLogxK
U2 - 10.1186/s41747-017-0025-2
DO - 10.1186/s41747-017-0025-2
M3 - Article
C2 - 29708200
AN - SCOPUS:85050193438
VL - 1
JO - European radiology experimental
JF - European radiology experimental
SN - 2509-9280
IS - 1
M1 - 22
ER -