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Radiomics in the Analysis of Breast Cancer Heterogeneity On DCE-MRI

H Li

H Li1*, L Lan1 , K Drukker1 , C Perou2 , M Giger1 , (1) University of Chicago, Chicago, IL, (2) University of North Carolina, Chapel Hill, NC


TU-AB-BRA-8 (Tuesday, July 14, 2015) 7:30 AM - 9:30 AM Room: Ballroom A

Purpose: To investigate the heterogeneity of breast tumors in terms of variation in contrast enhancement as observed in computer-extracted MRI-based tumor phenotypes.

Methods: Analysis was conducted on a retrospective dataset of 84 de-identified, multi-institutional breast magnetic-resonance images (MRIs) from the National Cancer Institute repository, The Cancer Imaging Archive, along with clinical, histopathological, and genomic data from The Cancer Genome Atlas and gene assay data. The 84 cases were classified into Normal-like, Luminal A, Luminal B, HER2-enriched, and Basal-like molecular subtypes based on gene expression classifications. For each case, analysis of the dynamic-contrast enhanced MRIs included computerized 3D lesion segmentation and phenotype extraction, which characterized tumor size, shape, morphology, enhancement texture, kinetic curve assessment, and enhancement variance kinetics of the breast tumors. Enhancement texture over the imaging sequence was calculated at the first, second and third post-contrast time frames. Associations between computer-extracted tumor phenotypes and molecular subtypes were assessed through inferences on the Kendall τ rank correlation coefficient.

Results: By examining the relationship between image-based phenotype and the molecular subtypes, there is a positive trend for enhancement texture for the various molecular subtypes. The Kendall τ rank correlation coefficient of 0.2342 with a p-value of 0.0055 was obtained between enhancement texture of entropy which was calculated at the first post-contrast time frame of DCE-MR image and the molecular subtypes. This enhancement texture quantitatively characterizes the heterogeneous nature of contrast uptake within the breast tumor.

Conclusion: Computer-extracted image-based phenotypes show promise as a means for high-throughput discrimination of breast cancer molecular subtypes.

Funding Support, Disclosures, and Conflict of Interest: Funding: University of Chicago Dean Bridge Fund, NCI U24-CA143848-05, and Breast Cancer Research Foundation. COI: MLG is a stockholder in R2 technology/Hologic and receives royalties from Hologic, GE Medical Systems, MEDIAN Technologies, Riverain Medical, Mitsubishi, and Toshiba. MLG is a co-founder and stockholder in Quantitative Insights.

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