Abstract
.We propose an intrinsic cancer marker in fixed tissue biopsy slides, which is based on the local spatial autocorrelation length obtained from quantitative phase images. The spatial autocorrelation length in a small region of the tissue phase image is sensitive to the nanoscale cellular morphological alterations and can hence inform on carcinogenesis. Therefore, this metric can potentially be used as an intrinsic cancer marker in histopathology. Typically, these correlation length maps are calculated by computing two-dimensional Fourier transforms over image subregions—requiring long computational times. We propose a more time-efficient method of computing the correlation map and demonstrate its value for diagnosis of benign and malignant breast tissues. Our methodology is based on highly sensitive quantitative phase imaging data obtained by spatial light interference microscopy.
Highlights
According to the World Health Organization, cancer is a major cause of death globally.[1]
We have presented an efficient algorithm for the computation of the local correlation length within refractive index maps of fixed breast tissue biopsy slides
Since in this work the refractive index maps are extracted using spatial light interference microscopy (SLIM), which has sub-nanometer optical path length sensitivity, the correlation length is indicative of nanoscale cellular morphology
Summary
According to the World Health Organization, cancer is a major cause of death globally.[1] Effective treatment strategies require early and accurate diagnosis of the disease. The gold standard method for cancer diagnosis is the microscopic investigation of a stained tissue biopsy by a trained clinical pathologist. Through this investigation, the pathologist looks for morphological signatures of either normal or abnormal tissue. The pathologist looks for morphological signatures of either normal or abnormal tissue Being qualitative, this type of assessment leads to interobserver discrepancy and to automation of some or part of the process through machine learning where image analysis is complicated by stain variability.[2,3] Ensuring consistency in the disease signatures extracted through image analysis of stained tissue remains a significant challenge due to variations in tissue preparation.[4]
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