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Radiomics of Breast Cancer: A Robustness Study

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N Antropova

N Antropova*, M Giger , H Li , K Drukker , L Lan , Univ Chicago, Chicago, IL


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

Purpose: Computer-extracted image phenotypes (CEIPs) are being investigated as complimentary attributes in the characterization of breast cancer in radiomics/ radiogenomics research. To be useful, CIEPs need to be robust across data obtained with different manufacturers’ MRI scanners and imaging protocols.

Methods: Our research involved two HIPAA-compliant retrospectively-collected MRI datasets: Database 1 included 91 imaged breast cancers from the National Cancer Institute repository (imaged using General Electric equipment) and Database 2 included 117 breast cancers (imaged at our site using Phillips equipment). For each case, information on clinical lymph node status and histopathology on ER, PR, and Her2 receptor status was available. Each lesion underwent quantitative radiomics analysis yielding CEIPs characterizing tumor size, shape, morphology, enhancement texture, kinetic curve assessment, and enhancement variance kinetics. The robustness of CEIPs was assessed through statistical comparisons across the two datasets in terms of average CEIP values, t-test results on the subgroups of interest, and non-inferiority testing of performance in the prognostic tasks of distinguishing ER, PR, and Her2 receptor status and lymph node status using area under the receiver operating characteristic curve (AUC).

Results: We failed to find any statistically significant differences in the average value of the CEIP distributions across the 2 scanners for subgroups possessing enough cases. We found greater variation in average feature values for the clinical subgroups having less than 20 cases. Non-inferiority analysis demonstrated varying degrees of robustness for different MRI phenotypes. The most enhancing volume and total rate variation showed the best agreement with absolute value of the lower bound of the 90% confidence for delta AUC<0.02.

Conclusion: MRI phenotypes appeared robust in their average values across MRI scanners given large enough datasets. Statistical analysis revealed the robust phenotypes in cancer subtype classification. In future work, larger data sets will be collected and robustness of CEIP further investigated.

Funding Support, Disclosures, and Conflict of Interest: Supported, in part, by the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health under grant number T32 EB002103.

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