# Digital Histology by Phase Imaging Specific Biomarkers for Human Tumoral Tissues Discrimination
###### Abstract  
Histology is the diagnosis gold standard. Conventional biopsy presents artifacts, delays, or human bias. Digital histology includes automation and improved diagnosis. It digitalizes microscopic images of histological samples and analyzes similar parameters. The present approach proposes the novel use of phase contrast in clinical digital histology to improve diagnosis. The use of label-free fresh tissue slices prevents processing artifacts and reduces processing time. Phase contrast parameters are implemented and calculated: the external scale, the fractal dimension, the anisotropy factor, the scattering coefficient, and the refractive index variance. Images of healthy and tumoral samples of liver, colon, and kidney are employed. A total of 252 images with 10\(\times\), 20\(\times\), and 40\(\times\) magnifications are measured. Discrimination significance between healthy and tumoral tissues is assessed statistically with ANOVA (\(p\)-value \(<\) 0.005). The analysis is made for each tissue type and for different magnifications. It shows a dependence on tissue type and image magnification. The \(p\)-value of the most significant parameters is below 10\({}^{-5}\). Liver and colon tissues present a great overlap in significant phase contrast parameters. The 10\(\times\) fractal dimension is significant for all tissue types under analysis. These results are promising for the use of phase contrast in digital histology clinical praxis. digital histology; phase contrast imaging; biomarkers; biomedical optics; fractal analysis 202116420211642021164216  
Label-free imaging could further improve digital histology analysis. It would prevent processing artifacts and reduce processing time. One of the problems is that biological tissues do not provide sufficient intrinsic contrast to distinguish the structures of interest, and staining is needed. Biological tissues are also intrinsically highly turbid, due to strong variations of refractive index. This fact complicates the observation of internal structures without physical sectioning. Optical scattering plays a significant role in light-tissue interactions. Light scattering techniques have been widely used for label-free diagnosis with different setups [3]. Some of the techniques include optical sectioning, such as Optical Coherence Tomography [4; 5]. The problem with optical sectioning is the lack of images with very high resolution: below 1 micron. Microscopic observations allow practically diffraction-limited resolution. The use of several intrinsic contrast parameters has also been proposed, such as polarimetry [6; 7], fluorescence [8; 9], or spectroscopy [10; 11; 12; 13]. However, the contrast of these techniques is usually limited and requires extrinsic biomarkers in the case of fluorescence. In this work, we propose a novel approach that consists of exploiting the high variation of refractive index of biological tissues for diagnosis while maintaining very high resolution. We propose to employ label-free fresh tissue slices and phase contrast imaging for biological sample analysis. This approach allows first to eliminate artifacts that may appear in the fixation and staining stages of conventional biopsy procedures. Those procedures can be avoided due to the intrinsic refractive index variance that phase contrast exploits. It also reduces the sample processing time. Phase contrast imaging provides novel tissue characteristics that are particularly relevant in morphstructural changes, which are typical in tumoral tissues. This technique could complement or even serve as the main basis for disease diagnosis [14]. The present approach is based on Quantitative Phase Imaging (QPI) techniques [15]. The power spectral density of the optical beam is modeled by a Von Karman spectrum that depends on both the optical beam and biological tissue parameters [16].