Intrinsic radiomics phenotypes of DCI from breast DCE-MRI: demonstrating feasibility in interim analysis of the ECOG-ACRIN E4112 trial

Vivian Belenky; Rhea Chitalia; Nickolas Lewis; Debosmita Biswas; Constantine Gatsonis; Jennifer Xiao; Michael Hirano; Sunil Badve; Joseph A. Sparano; Seema Khan; Kathy D. Miller; Constance Lehman; Justin Romanoff; Antonio C. Wolff; Christopher Comstock; Savannah C. Partridge; Habib Rahbar; Despina Kontos

doi:10.1117/12.2582000

15 February 2021 Intrinsic radiomics phenotypes of DCI from breast DCE-MRI: demonstrating feasibility in interim analysis of the ECOG-ACRIN E4112 trial

Vivian Belenky, Rhea Chitalia, Nickolas Lewis, Debosmita Biswas, Constantine Gatsonis, Jennifer Xiao, Michael Hirano, Sunil Badve, Joseph A. Sparano, Seema Khan, Kathy D. Miller, Constance Lehman, Justin Romanoff, Antonio C. Wolff, Christopher Comstock, Savannah C. Partridge, Habib Rahbar, Despina Kontos

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Proceedings Volume 11597, Medical Imaging 2021: Computer-Aided Diagnosis; 115970B (2021) https://doi.org/10.1117/12.2582000
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

Ductal in-situ carcinoma (DCIS) is a non-invasive proliferation that lacks the ability to metastasize. Over the past four decades, DCIS diagnoses have increased ten-fold, with treatments nearly as aggressive as those for small low-grade invasive breast cancer. In this study, we evaluate the potential of identifying intrinsic imaging phenotype of DCIS using radiomic signatures from breast DCE-MRI. The rationale is that such phenotypes may capture aspects of the heterogeneity of DCIS that can aid in identifying indolent from aggressive disease to better stratify patients for improved disease management. An initial analysis was performed on eighty- two DCIS cases from the ECOG-ACRIN E4112 trial. The Cancer Phenomics Toolkit (CapTK) was used to extract a total of 95 3-D radiomic features from each primary lesion volume in pre-treatment, pre-operative breast DCE-MRI images. Features were first filtered for robustness across the heterogeneous clinical sites of DCE-MRI acquisition and features deemed non-robust (59) were discarded. Dimensionality reduction was performed with the remaining thirty-six features via principle component analysis (PCA). Unsupervised hierarchical clustering of the resulting five principal components (PCs) capturing 85% of the original feature variance was applied. Two significant intrinsic DCIS radiomic phenotypes were identified (p<0.001). Our hypothesis is that DCIS imaging biomarkers could improve prognostic ability more reliably than biopsy alone. These findings will be further explored in the expanded analysis of ECOG-ACRIN E4112 trial.

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Vivian Belenky, Rhea Chitalia, Nickolas Lewis, Debosmita Biswas, Constantine Gatsonis, Jennifer Xiao, Michael Hirano, Sunil Badve, Joseph A. Sparano, Seema Khan, Kathy D. Miller, Constance Lehman, Justin Romanoff, Antonio C. Wolff, Christopher Comstock, Savannah C. Partridge, Habib Rahbar, and Despina Kontos "Intrinsic radiomics phenotypes of DCI from breast DCE-MRI: demonstrating feasibility in interim analysis of the ECOG-ACRIN E4112 trial", Proc. SPIE 11597, Medical Imaging 2021: Computer-Aided Diagnosis, 115970B (15 February 2021); https://doi.org/10.1117/12.2582000

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