Quantitatively distinguishing typical pathological features between different breast tissues using polarimetry feature parameters

摘要

Breast diseases with many distinct histopathological types are showing a rising trend in incidence for decades worldwide. The proliferation of cells and the remodeling of collagen fibers in breast carcinoma tissues may be used to predict breast disease diagnosis, prognosis of treatment, and patient survival. Pathologists can label related typical pathological features as cell nuclei, aligned collagen, and disorganized collagen in hematoxylin and eosin (H&E) sections of breast tissues. In this study, we apply the Mueller matrix microscopic imaging to various breast pathological section samples, and calculate corresponding polarimetry basis parameters (PBPs). A pixel-based extraction approach of polarimetry feature parameters (PFPs) is proposed using a mutual information (MI) method and a linear discriminant analysis (LDA) classifier. The three PFPs derived by the proposed learning algorithm are the simplified linear combinations of PBPs with physical meanings, and provide quantitative characterization of the three pathological features in different breast tissues respectively. We present results of the three PFPs of tissue samples from a cohort of 32 clinical patients diagnosed as normal, breast fibroma, breast ductal carcinoma in situ, invasive ductal carcinoma, and breast mucinous carcinoma with analysis of 210 regions-of-interest (ROI). The results demonstrate that the three PFPs of each breast disease tissue have specific value ranges, which has a potential to quantitatively distinguish typical pathological features between different breast tissues. This technique has good prospects for automation of the microstructure identification and prediction of breast disease diagnosis, resulting in the reduction of pathologists' workload.