Mammogram Quantitative Features Associated with Histological High-Grade Breast Cancer

Abstract

High grade breast cancer is recognized as more aggressive cancer type and is the worst survival prognostic. To explore the association of quantitative features extracted from mammograms with histological high-grade breast cancer. We conducted a retrospective study using an open source data got from figshare repository. These anonymized data were collected and used for a study approved by the institutional review board. Cranio-Caudal (CC) and Medio-lateral (MLO) mammograms and their tumor segmented images from 66 patients subdivided in two groups high histological grade (n=23) low-grade (low and intermediate, n=41). From breast cancer image segmentation, we extracted 480 features using python software radiomics package Pyradiomics 2.2. With the features extracted from CC and MLO images, we used them separately for histological high-grade breast, relevant feature selection. We performed univariate feature selection based on ANOVA test using machine learning python package: sklearn. A feature was considered relevant when P value is at least 0.05. At the end we represented the boxplot of the distribution of the low-and high-grade subject using each relevant feature selected. Twenty (20) CC images features were selected, seventen (17) were based on wavelets and three (3) were from original image. Their p values were ranged between 0.017 and 0.046. In the case of MLO features, four (04) relevant features were exclusively based on wavelets with 0.046 as the maximum p-value and 0.006 as minimum. These results suggested mammogram quantitative feature based on wavelets will be useful for high-grade breast cancer identification on mammographic image. In this study we explored the association between IBSI 2D quantitative features from mammogram with the histological high-grade breast cancer. Finally, we recorded twenty (20) relevant features from CC projection and four for MLO mammogram projection. Wavelets based features were more represented in relevant quantitative feature.