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.
While breast cancer screening recommendations vary by agency, all agencies recommend mammographic screening with some frequency over some portion of a woman’s lifetime. Temporal evaluation of these images may inform personalized risk of breast cancer. However, due to the highly deformable nature of breast tissue, the positioning of breast tissue may vary widely between exams. Therefore, registration of physical regions in the breast over time points is a critical first step in computerized analysis of changes in breast parenchyma over time. While a postregistration image is altered and therefore not appropriate for radiomic texture analysis, the registration process produces a mapping of points which may aid in aligning similar image regions across multiple time points. In this study, a total of 633 mammograms from 87 patients were retrospectively collected. These images were sorted into 1144 temporal pairs, where each combination of images of a given women of a given laterality was used to form a temporal pair. B-splines registration and multi-resolution registration were performed on each mammogram pair. While the B-splines took an average of 552.8 CPU seconds per registration, multi-resolution registration took only an average of 346.2 CPU seconds per registration. Multi-resolution registration had a 15% lower mean square error, which was significantly different than that of B-splines (p<0.001). While previous work aimed to allow radiologists to visually evaluate the registered images, this study identifies corresponding points on images for use in assessing interval change for risk assessment and early detection of cancer through deep learning and radiomics.
Extraction of high-dimensional quantitative data from medical images has become necessary in disease risk assessment,
diagnostics and prognostics. Radiomic workflows for mammography typically involve a single medical image for each
patient although medical images may exist for multiple imaging exams, especially in screening protocols. Our study
takes advantage of the availability of mammograms acquired over multiple years for the prediction of cancer onset. This
study included 841 images from 328 patients who developed subsequent mammographic abnormalities, which were
confirmed as either cancer (n=173) or non-cancer (n=155) through diagnostic core needle biopsy. Quantitative radiomic
analysis was conducted on antecedent FFDMs acquired a year or more prior to diagnostic biopsy. Analysis was limited
to the breast contralateral to that in which the abnormality arose. Novel metrics were used to identify robust radiomic
features. The most robust features were evaluated in the task of predicting future malignancies on a subset of 72 subjects
(23 cancer cases and 49 non-cancer controls) with mammograms over multiple years. Using linear discriminant analysis,
the robust radiomic features were merged into predictive signatures by: (i) using features from only the most recent
contralateral mammogram, (ii) change in feature values between mammograms, and (iii) ratio of feature values over
time, yielding AUCs of 0.57 (SE=0.07), 0.63 (SE=0.06), and 0.66 (SE=0.06), respectively. The AUCs for temporal
radiomics (ratio) statistically differed from chance, suggesting that changes in radiomics over time may be critical for
risk assessment. Overall, we found that our two-stage process of robustness assessment followed by performance
evaluation served well in our investigation on the role of temporal radiomics in risk assessment.