Parenchymal field effect analysis for breast cancer risk assessment: evaluation of FFDM radiomic similarity

Natalie M. Baughan; Hui Li; Li Lan; Chun-Wai Chan; Matthew Embury; Gary Whitman; Randa El-Zein; Isabelle Bedrosian; Maryellen L. Giger

doi:10.1117/12.2581791

15 February 2021 Parenchymal field effect analysis for breast cancer risk assessment: evaluation of FFDM radiomic similarity

Natalie M. Baughan, Hui Li, Li Lan, Chun-Wai Chan, Matthew Embury, Gary Whitman, Randa El-Zein, Isabelle Bedrosian, Maryellen L. Giger

Author Affiliations +

Proceedings Volume 11597, Medical Imaging 2021: Computer-Aided Diagnosis; 115970Z (2021) https://doi.org/10.1117/12.2581791
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

Histologically normal areas of the breast parenchyma have been shown to share molecular similarity with breast tumors, suggesting the presence of a field effect in breast cancer. To further understand a potential cancer field effect, we compared mammographic parenchymal texture features across four regions of the breast. The study included 103 FFDMs with at least one identified malignant tumor. All FFDM images (12-bit quantization and 70 micron pixels) were acquired with a Hologic Lorad Selenia system and retrospectively collected under an IRB-approved protocol. Regions of interest (ROI) of 128x128 and 256x256 pixels were selected from four regions across the craniocaudal projection: within the identified tumor, adjacent to the tumor, distant from the tumor, and behind the nipple in the contralateral breast. Radiographic texture analysis was used to extract 45 features in each region. Kolmogorov-Smirnov (KS) and Pearson correlation tests assessed similarity between features in each region. KS test results, with a 95% confidence interval on the KS test statistic bootstrapped with 2000 iterations indicated that 81.8% (128x128) and 88.4% (256x256) of feature distributions across all ROI regions showed equivalence with a threshold equal to the critical value at the p = 0.05 level. Pearson correlation results demonstrated a majority of structure-based feature comparisons which reached statistical significance, and less intensity-based feature comparisons which reached statistical significance. These results support our hypothesis of a potential cancer field effect across tumor and non-tumor regions and support the development of computerized analysis of mammographic parenchymal patterns to assess breast cancer risk.

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Natalie M. Baughan, Hui Li, Li Lan, Chun-Wai Chan, Matthew Embury, Gary Whitman, Randa El-Zein, Isabelle Bedrosian, and Maryellen L. Giger "Parenchymal field effect analysis for breast cancer risk assessment: evaluation of FFDM radiomic similarity", Proc. SPIE 11597, Medical Imaging 2021: Computer-Aided Diagnosis, 115970Z (15 February 2021); https://doi.org/10.1117/12.2581791

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