PurposeIn women with biopsy-proven breast cancer, histologically normal areas of the parenchyma have shown molecular similarity to the tumor, supporting a potential cancer field effect. The purpose of this work was to investigate relationships of human-engineered radiomic and deep learning features between regions across the breast in mammographic parenchymal patterns and specimen radiographs.ApproachThis study included mammograms from 74 patients with at least 1 identified malignant tumor, of whom 32 also possessed intraoperative radiographs of mastectomy specimens. Mammograms were acquired with a Hologic system and specimen radiographs were acquired with a Fujifilm imaging system. All images were retrospectively collected under an Institutional Review Board-approved protocol. Regions of interest (ROI) of 128 × 128 pixels were selected from three regions: within the identified tumor, near to the tumor, and far from the tumor. Radiographic texture analysis was used to extract 45 radiomic features and transfer learning was used to extract 20 deep learning features in each region. Kendall’s Tau-b and Pearson correlation tests were performed to assess relationships between features in each region.ResultsStatistically significant correlations in select subgroups of features with tumor, near to the tumor, and far from the tumor ROI regions were identified in both mammograms and specimen radiographs. Intensity-based features were found to show significant correlations with ROI regions across both modalities.ConclusionsResults support our hypothesis of a potential cancer field effect, accessible radiographically, across tumor and non-tumor regions, thus indicating the potential for computerized analysis of mammographic parenchymal patterns to predict breast cancer risk.
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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