Next-Generation Morphometry for pathomics-data mining in histopathology

David L. Hölscher, Nassim Bouteldja, Mehdi Joodaki, Maria L. Russo, Yu-Chia Lan, Alireza Vafaei Sadr, Mingbo Cheng, Vladimir Tesar, Saskia V. Stillfried, Barbara M. Klinkhammer, Jonathan Barratt, Jürgen Floege, Ian S. D. Roberts, Rosanna Coppo, Ivan G. Costa, Roman D. Bülow and Peter Boor ()
Additional contact information
David L. Hölscher: RWTH Aachen University Clinic
Nassim Bouteldja: RWTH Aachen University Clinic
Mehdi Joodaki: RWTH Aachen University Clinic
Maria L. Russo: Fondazione Ricerca Molinette
Yu-Chia Lan: RWTH Aachen University Clinic
Alireza Vafaei Sadr: RWTH Aachen University Clinic
Mingbo Cheng: RWTH Aachen University Clinic
Vladimir Tesar: 1st Faculty of Medicine and General University Hospital, Charles University
Saskia V. Stillfried: RWTH Aachen University Clinic
Barbara M. Klinkhammer: RWTH Aachen University Clinic
Jonathan Barratt: University Hospital of Leicester National Health Service Trust
Jürgen Floege: RWTH Aachen University Clinic
Ian S. D. Roberts: Oxford University Hospitals National Health Services Foundation Trust
Rosanna Coppo: Fondazione Ricerca Molinette
Ivan G. Costa: RWTH Aachen University Clinic
Roman D. Bülow: RWTH Aachen University Clinic
Peter Boor: RWTH Aachen University Clinic

Nature Communications, 2023, vol. 14, issue 1, 1-14

Abstract: Abstract 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.

Date: 2023
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DOI: 10.1038/s41467-023-36173-0

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