Program Information
Prognostic Radiomics of Breast Cancer On DCE and DWI MR Images
N Maforo*, H Li , L Lan , A Edwards, M L Giger , University Chicago, Chicago, IL
Presentations
SU-F-R-26 (Sunday, July 31, 2016) 3:00 PM - 6:00 PM Room: Exhibit Hall
Purpose: For this study, we investigated quantitative radiomics of breast tumors on diffusion weighted imaging and dynamic contrast-enhanced MRIs in the task of assessing the prognostic status of breast cancers.
Methods:: Our IRB-approved, retrospectively-collected dataset included 316 breast cancers with 235 ER+ and 81 ER- cases. All images were acquired during clinical breast MRI incorporating dynamic-contrast MRI and diffusion-weighted MRI. Phenotypic categories extracted quantitatively from DCE-MRI included tumor size, shape, margin sharpness, enhancement texture, kinetics, and variance kinetics, and from DWI-DCE ADC features (average, range, variation) for DWI. Phenotypes, as well as merged tumor signatures from round robin evaluation, were assessed for the prognostic tasks using area under the ROC curve (AUC) as the index of performance.
Results:In the task of distinguishing between ER+ and ER- cancers, computer-extracted phenotypes from DCE and DWI yielded comparable performance levels, however, we found that the phenotypes, as well as the modality-specific tumor signatures, showed only slight correlation (r=-0.44), thus indicating the promise of multi-modality signatures. In the tasks of ER+ vs. ER-. PR+ vs. PR-, lymph node positive vs negative, we obtained AUC values of 0.66 (0.03), 0.64 (0.03), and 0.64 (0.03) for DCE-MRI, and AUC values of 0.64 (0.03), 0.61 (0.03), and 0.61 (0.03) for DWI-MRI, respectively. The combination of the modalities yielded AUC values of 0.67 (0.03), 0.64 (0.03), and 0.62 (0.03), respectively.
ConclusionThe correlation and performance results obtained from merging radiomic features from DCE-MRI and DWI-MRI indicate that the additional benefit of multimodality breast MRI in assessing prognosis is promising.
Funding Support, Disclosures, and Conflict of Interest: Funded by an NIH (PREP) (R25) Grant and the University of Chicago Dean Bridge Fund. COI: M.L. Giger is a stockholder in R2 technology/Hologic and receives royalties from Hologic, GE Medical Systems, MEDIAN Technologies, Riverrain Medical, Mitsubushi, and Toshiba. MLG is a co-founder and stockholder in Quantitative Insights.
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