Quantification of structural heterogeneity in H&E stained clear cell renal cell carcinoma using refractive index tomography

Abstract

Clear cell renal cell carcinoma (ccRCC) is a common histopathological subtype of renal cancer and is notorious for its poor prognosis. Its accurate diagnosis by histopathology, which relies on manual microscopic inspection of stained slides, is challenging. Here, we present a correlative approach to utilize stained images and refractive index (RI) tomography and demonstrate quantitative assessments of the structural heterogeneities of ccRCC slides obtained from human patients. Machine-learning-assisted segmentation of nuclei and cytoplasm enabled the quantification at the subcellular level. Compared to benign regions, malignant regions exhibited a considerable increase in structural heterogeneities. The results demonstrate that RI tomography provides quantitative information in synergy with stained images on the structural heterogeneities in ccRCC.

Quantitative Phase Imaging Using Digital Holographic Microscopy to Assess the Degree of Intestinal Inflammation in Patients with Ulcerative Colitis

“…Moreover, it enables the quantification of tissue density based on refractive index determination with minimized sample preparation efforts [ 22 , 51 , 52 ]. Park et al demonstrated that QPI offers quantitative information in an automated manner in the diagnosis of clear-cell renal cell carcinoma [ 53 ], a disease that is difficult to diagnose using common microscopic histopathological evaluation. Based on our findings, QPI could play a similar role when it comes to quantifying inflammation on a microscopic level in patients with UC.…”

Label‐free histological analysis of retrieved thrombi in acute ischemic stroke using optical diffraction tomography and deep learning

“…This work exploits the high-resolution and quantitative 3D imaging capability of ODT, which extract various quantitative biophysical properties from the RI distribution [10]. These strengths of label-free imaging and ODT had been demonstrated in histological studies [12][13][14][15][16][17], including kidney cancer [18], kidney injury [19], and intestinal inflammation [20]. The use of RI as reproducible and quantitative imaging contrast has strengths when combined with DL.…”

Label-free histological analysis of retrieved thrombi in acute ischemic stroke using optical diffraction tomography and deep learning

“…This work exploits the high-resolution and quantitative 3D imaging capability of ODT, which extract various quantitative biophysical properties from the RI distribution [10]. These strengths of label-free imaging and ODT had been demonstrated in histological studies [12][13][14][15][16][17], including kidney cancer [18], kidney injury [19], and intestinal inflammation [20]. The use of refractive index as reproducible and quantitative imaging contrast has strengths when combined with DL.…”