Tissue spatial correlation as cancer marker

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. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

Shear Modulus Measurement by Quantitative Phase Imaging and Correlation with Atomic Force Microscopy

“…As such, in the context of phase imaging, L D can be seen as a measure of the variance of optical pathlength. Disorder strength has been previously used to distinguish histopathologically normal tissue from cancerous or precancerous tissue (35)(36)(37)(38)(39)(40). Furthermore, a link between structural disorder and cellular elasticity has been established by Gupta et al, who found a correlation between cytoskeletal ordering and traction forces generated by cells (41).…”

Automatic colorectal cancer screening using deep-learning on spatial light interference microscopy data

“…Due its sensitivity to tissue nanoarchitecture and ability to provide quantitative information from 3D and anisotropic tissue structures [14][15][16], QPI has been applied to different pathology problems in recent years [12,[17][18][19][20][21][22][23][24]. Spatial light interference microscopy (SLIM) [25][26][27] has been used as the core technology for label-free whole slide imaging (WSI), which revealed that tissue refractive index is an effective intrinsic marker for pathological diagnosis and prognosis [21,22,[28][29][30][31][32][33][34]. It was shown that tissue scattering coefficients computed from the QPI data were able to predict disease recurrence after prostatectomy surgery [20,21].…”

Monitoring reactivation of latent HIV by label-free gradient light interference microscopy

“…QPI has been applied to investigations of cell growth (Kandel et al, 2019c;Lee et al, 2017;Mir et al, 2011;Sridharan Weaver et al, 2019), neuron dynamics Fan et al, 2017;Hu and Popescu, 2018;Hu et al, 2019a;Wang et al, 2011a), intracellular mass transport (Wang et al, 2011b), red blood cell properties (Popescu et al, 2005(Popescu et al, , 2006(Popescu et al, , 2007, fertility outcomes in cattle (Rubessa et al, 2019(Rubessa et al, , 2020, etc. Since it provides intrinsic markers such as dry mass and optical path length change, it has been successful in revealing new and crucial information in histopathology (Majeed et al, 2019;Takabayashi et al, 2018Takabayashi et al, , 2019, prostate cancer (Nguyen et al, 2017c), breast cancer (Majeed et al, 2018), colorectal cancer (Kandel et al, 2017), pancreatic cancer (Fanous et al, 2020), skin cancer (Li et al, 2019), blood screenings (Mir et al, 2010), pelvic organ prolapse (Hu et al, 2019b), and kidney injury (Ban et al, 2018). Recent advances in deep learning allow the development of phase imaging with computational specificity, where synthetic fluorescence is generated computationally from label-free data (Kandel et al, 2020).…”