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Next-Generation Morphometry for pathomics-data mining in histopathology

DL. Hölscher, N. Bouteldja, M. Joodaki, ML. Russo, YC. Lan, AV. Sadr, M. Cheng, V. Tesar, SV. Stillfried, BM. Klinkhammer, J. Barratt, J. Floege, ISD. Roberts, R. Coppo, IG. Costa, RD. Bülow, P. Boor

. 2023 ; 14 (1) : 470. [pub] 20230128

Language English Country England, Great Britain

Document type Journal Article, Research Support, Non-U.S. Gov't

Persistent link   https://www.medvik.cz/link/bmc23004496

Pathology diagnostics relies on the assessment of morphology by trained experts, which remains subjective and qualitative. Here we developed a framework for large-scale histomorphometry (FLASH) performing deep learning-based semantic segmentation and subsequent large-scale extraction of interpretable, quantitative, morphometric features in non-tumour kidney histology. We use two internal and three external, multi-centre cohorts to analyse over 1000 kidney biopsies and nephrectomies. By associating morphometric features with clinical parameters, we confirm previous concepts and reveal unexpected relations. We show that the extracted features are independent predictors of long-term clinical outcomes in IgA-nephropathy. We introduce single-structure morphometric analysis by applying techniques from single-cell transcriptomics, identifying distinct glomerular populations and morphometric phenotypes along a trajectory of disease progression. Our study provides a concept for Next-generation Morphometry (NGM), enabling comprehensive quantitative pathology data mining, i.e., pathomics.

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$a Joodaki, Mehdi $u Institute for Computational Genomics, RWTH Aachen University Clinic, Aachen, Germany
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$a Russo, Maria L $u Fondazione Ricerca Molinette, Torino, Italy
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$a Lan, Yu-Chia $u Institute of Pathology, RWTH Aachen University Clinic, Aachen, Germany
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$a Sadr, Alireza Vafaei $u Institute of Pathology, RWTH Aachen University Clinic, Aachen, Germany
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