Association between Radiological and First-Order Statistical Features of the Mammogram, and the Tumor Phenotype in Breast Cancer Patients

Background: Breast cancer is among the most prevalent cancers which can effectively be screened by mammography. The spatial distribution of grey levels of the mammogram known as first-order statistical features (FOSFs) contain higher dimensional data which describe the breast composition. We aim to test these basic measures to differentiate density categories in breast cancer patients, and use them as covariates to investigate the relationship between radiologic and pathologic features of the tumor. Methods: Data from Eighty-five breast cancer subjects, their BI-RADS breast density category (a to d), percentage density (PD), and FOSFs of the mammogram including median, mean, interquartile range, kurtosis, maximum, minimum, standard deviation, skewness, and energy, were extracted. The tumor grade and the percentage of Ki67, ER, PR, and Her2 status were recorded. A linear discriminant analysis, and a support vector machine (SVM) were used to discriminate each density category from others. Then, the relation between variables were investigated using ANCOVA and regression analysis. Results: Density categories a and d were classified by SVM with high accuracy. The key feature of a and d were interquartile range and maximum intensities respectively. Reported tumor margins were related to Her2 overexpression and PR positivity. Spiculated tumor margin predicted percentage of PR expression, with a cumulative odds ratio of 7.85 (CI 2.5- 24.78), when adjusted for age, area of breast, density, and FOSFs, (p=0.0004). Conclusion: The findings of this study suggest that FOSFs can be incorporated in computer-aided systems to adjust for differences in breast composition and to refine risk profiles.