Abstract
BackgroundIn 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.MethodsOur 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.ResultsInter-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.ConclusionsQuantitative radiomics of breast cancer may replicate human-extracted tumour size and BI-RADS imaging phenotypes, thus enabling precision medicine.
Highlights
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
The purpose of this study was to investigate if computer-extracted image phenotypes (CEIP) MRI phenotypes of breast cancer could replicate human-extracted imaging phenotypes (HEIP) using MRIs from The Cancer Genome Atlas (TCGA) project of the National Cancer Institute
We report new findings from our study done to investigate if CEIP of breast cancer could replicate HEIP using MRI from TCGA
Summary
Substantial inter- and intra-observer agreement variability continue to exist among radiologists, though the degree of both is unclear because of a paucity of literature [3]. Despite this recognised variability, the radiologist is the imaging reference standard for interpretation of diagnostic imaging studies, including breast MRI. The goal of research focusing on integrating computeraided diagnosis (CAD) and human MRI interpretation is to improve breast cancer detection, moving beyond determining if a lesion is benign or malignant [4,5,6,7], and to use radiomics in assessing cancer subtypes. There may be computer software and/or algorithm variability
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