Large-scale extraction of interpretable features provides new insights into kidney histopathology – A proof-of-concept study

Gupta L, Klinkhammer BM, Seikrit C, Fan N, Bouteldja N, Gräbel P, Gadermayr M, Boor P, Merhof D (2022)

Publication Type: Journal article

Publication year: 2022

Journal

Journal of Pathology Informatics Medknow Publications

Book Volume: 13

Article Number: 100097

DOI: 10.1016/j.jpi.2022.100097

Abstract

Whole slide images contain a magnitude of quantitative information that may not be fully explored in qualitative visual assessments. We propose: (1) a novel pipeline for extracting a comprehensive set of visual features, which are detectable by a pathologist, as well as sub-visual features, which are not discernible by human experts and (2) perform detailed analyses on renal images from mice with experimental unilateral ureteral obstruction. An important criterion for these features is that they are easy to interpret, as opposed to features obtained from neural networks. We extract and compare features from pathological and healthy control kidneys to learn how the compartments (glomerulus, Bowman's capsule, tubule, interstitium, artery, and arterial lumen) are affected by the pathology. We define feature selection methods to extract the most informative and discriminative features. We perform statistical analyses to understand the relation of the extracted features, both individually, and in combinations, with tissue morphology and pathology. Particularly for the presented case-study, we highlight features that are affected in each compartment. With this, prior biological knowledge, such as the increase in interstitial nuclei, is confirmed and presented in a quantitative way, alongside with novel findings, like color and intensity changes in glomeruli and Bowman's capsule. The proposed approach is therefore an important step towards quantitative, reproducible, and rater-independent analysis in histopathology.

Involved external institutions

Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen DE Germany (DE) Universitätsklinikum Aachen (UKA) DE Germany (DE)

How to cite

APA:

Gupta, L., Klinkhammer, B.M., Seikrit, C., Fan, N., Bouteldja, N., Gräbel, P.,... Merhof, D. (2022). Large-scale extraction of interpretable features provides new insights into kidney histopathology – A proof-of-concept study. Journal of Pathology Informatics, 13. https://doi.org/10.1016/j.jpi.2022.100097

MLA:

Gupta, Laxmi, et al. "Large-scale extraction of interpretable features provides new insights into kidney histopathology – A proof-of-concept study." Journal of Pathology Informatics 13 (2022).

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