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Radiomics Investigation in the Distinction Between in Situ and Invasive Breast Cancers

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J Schram

J Schram*, K Drukker , S Burda , H Li , L Lan , M Giger , Univ Chicago, Chicago, IL


TU-CD-BRB-3 (Tuesday, July 14, 2015) 10:15 AM - 12:15 PM Room: Ballroom B

Purpose: To determine the capability of machine learning and computer-extracted image phenotypes to distinguish between ductal carcinoma in situ and invasive breast cancers, which may facilitate timely assessment of disease prognosis and treatment.

Methods: This study used a HIPAA compliant data set of 248 breast dynamic contrast-enhanced MR images of breast cancers collected under IRB-approved protocols (58 in situ and 190 invasive cancers). After automated 3D tumor segmentation, 38 image phenotypes were computer-extracted, describing tumor size, shape, morphology, enhancement texture, variance kinetics, and kinetic curve characteristics. A random forest classifier was used in a leave-one-case-out training/testing paradigm for the distinction between in situ and invasive breast cancer. Performance for this task was assessed using the area under the receiver operating characteristic curve (AUC). The random forest classifier also automatically determined the relevance of all 38 phenotypes to the task at hand.

Results: The random forest classifier obtained an AUC of 0.90 (95% confidence interval [0.86;0.95]) in the task of distinguishing between in situ and invasive breast cancers. Phenotypes most important here were shape (sphericity), enhancement texture (variance, difference variance, heterogeneity, contrast), and kinetic curve characteristics (normalized total rate variance, washout rate, time to peak). Phenotypes least effective were size (maximum diameter, surface area), morphology (margin gradient, margin sharpness), variance kinetics, and a kinetic curve characteristic (maximum enhancement).

Conclusion: We obtained promising results in automated MR image-based assessment of breast tumor invasiveness. In the current era of personalized medicine such analysis may positively impact patient care by identifying which breast tumors require timely work-up because of invasive components, and which findings could potentially be monitored without aggressive treatment. The image-based phenotypes that emerged in this study are markedly different from those relevant to the distinction between benign and malignant breast tumors, where mainly size and morphology influenced diagnostic decision making.

Funding Support, Disclosures, and Conflict of Interest: Funding: NIH S10 RR021039, University of Chicago Dean Bridge Fund. and Carole Segal. COI: M.L.G. 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 cofounder of and stockholder in Quantitative Insights.

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