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Radiomics of Multi-Parametric Breast MRI in Breast Cancer Diagnosis: A Quantitative Investigation of Diffusion Weighted Imaging, Dynamic Contrast-Enhanced, and T2-Weighted Magnetic Resonance Imaging

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N Maforo

N Maforo1*, H Li2 , W Weiss3 , L Lan4 , M Giger5 , (1) Fort Hays State University, Hays, KS (2) University of Chicago, Chicago, IL, (3) University of Chicago, Chicago, IL, (4) University of Chicago, Chicago, IL, (5) University of Chicago, Chicago, IL


SU-D-BRA-2 (Sunday, July 12, 2015) 2:05 PM - 3:00 PM Room: Ballroom A

Purpose: For this study, we investigated the computer-extracted tumor phenotypes from diffusion weighted imaging, dynamic contrast-enhanced, and T2-weighted magnetic resonance imaging modalities on a dataset of malignant and benign breast lesions.

Methods: The IRB-approved, retrospectively-collected dataset included 118 breast lesions with 105 malignant and 13 benign. All images were acquired during clinical breast MRI at both 1.5T and 3.0T magnet strength. Phenotypic categories extracted with each modality included tumor size, shape, margin sharpness, enhancement texture, kinetics, and variance kinetics for DCE, size, shape, margin sharpness, texture for T2w, and ADC features for DWI.

Results: In the task of distinguishing between benign and malignant lesions, each modality’s performance was analyzed by Round Robin evaluation using Receiver Operating Characteristic (ROC) analysis. DCE alone outperformed DWI and T2w with an AUC value of 0.89 +/- 0.06. DWI and T2w yielded AUC values of 0.86 +/- 0.05 and 0.84 +/- 0.06 respectively. The combination of all three modalities yielded an AUC value of 0.88 +/- 0.04 under single-loop Round Robin evaluation. The contrast phenotype from T2w and the standard deviation phenotype from DWI were found to be statistically different between the malignant and benign multimodality lesion groups.

Conclusion: The results obtained from merging radiomic features from multimodality breast MRI (DCE, T2w, and DWI) indicate that the additional benefit of multimodality breast MRI in cancer diagnosis could be significant. This method also has potential to determine the most discriminatory radiomic phenotype from each modality.

Funding Support, Disclosures, and Conflict of Interest: APPM DREAM Fellowship and the University of Chicago Dean Bridge Fund. M. L. Giger 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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