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.

Department of Cardiovascular Sciences University of Leicester Leicester United Kingdom

Department of Cellular Pathology Oxford University Hospitals National Health Services Foundation Trust Oxford United Kingdom

Department of Nephrology 1st Faculty of Medicine and General University Hospital Charles University Prague Czech Republic

Department of Nephrology and Immunology RWTH Aachen University Clinic Aachen Germany

Fondazione Ricerca Molinette Torino Italy

Institute for Computational Genomics RWTH Aachen University Clinic Aachen Germany

Institute of Pathology RWTH Aachen University Clinic Aachen Germany

John Walls Renal Unit University Hospital of Leicester National Health Service Trust Leicester United Kingdom

Regina Margherita Children's University Hospital Torino Italy

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Kidney Int. 2023 Dec;104(6):1050-1053. doi: 10.1016/j.kint.2023.06.006. PubMed

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Roufosse C, et al. A 2018 Reference Guide to the Banff Classification of Renal Allograft Pathology. Transplantation. 2018;102:1795–1814. PubMed PMC

Working Group of the International IgA Nephropathy Network and the Renal Pathology Society. et al. The Oxford classification of IgA nephropathy: rationale, clinicopathological correlations, and classification. Kidney Int. 2009;76:534–545. PubMed

Loupy A, et al. The Banff 2019 Kidney Meeting Report (I): Updates on and clarification of criteria for T cell- and antibody-mediated rejection. Am. J. Transpl. 2020;20:2318–2331. PubMed PMC

Trimarchi H, et al. Oxford Classification of IgA nephropathy 2016: an update from the IgA Nephropathy Classification Working Group. Kidney Int. 2017;91:1014–1021. PubMed

Bellur SS, et al. Reproducibility of the Oxford classification of immunoglobulin A nephropathy, impact of biopsy scoring on treatment allocation and clinical relevance of disagreements: evidence from the VALidation of IGA study cohort. Nephrol. Dial. Transpl. 2019;34:1681–1690. PubMed

Wilhelmus S, et al. Interobserver agreement on histopathological lesions in class III or IV lupus nephritis. Clin. J. Am. Soc. Nephrol. 2015;10:47–53. PubMed PMC

Boor P. Artificial intelligence in nephropathology. Nat. Rev. Nephrol. 2020;16:4–6. PubMed

Barisoni L, Lafata KJ, Hewitt SM, Madabhushi A, Balis UGJ. Digital pathology and computational image analysis in nephropathology. Nat. Rev. Nephrol. 2020;16:669–685. PubMed PMC

van der Laak J, Litjens G, Ciompi F. Deep learning in histopathology: the path to the clinic. Nat. Med. 2021;27:775–784. PubMed

Bulten W, et al. Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study. Lancet Oncol. 2020;21:233–241. PubMed

Kanavati F, et al. A deep learning model for the classification of indeterminate lung carcinoma in biopsy whole slide images. Sci. Rep. 2021;11:8110. PubMed PMC

Kather JN, et al. Pan-cancer image-based detection of clinically actionable genetic alterations. Nat. Cancer. 2020;1:789–799. PubMed PMC

Wang F, Kaushal R, Khullar D. Should health care demand interpretable artificial intelligence or accept “black box” medicine? Ann. Intern. Med. 2020;172:59–60. PubMed

Basu S, Kolouri S, Rohde GK. Detecting and visualizing cell phenotype differences from microscopy images using transport-based morphometry. Proc. Natl Acad. Sci. USA. 2014;111:3448–3453. PubMed PMC

Ilić S, Stojiljković N, Sokolović D, Jovanović I, Stojanović N. Morphometric analysis of structural renal alterations and beneficial effects of aminoguanidine in acute kidney injury induced by cisplatin in rats. Can. J. Physiol. Pharmacol. 2020;98:117–123. PubMed

Rawat RR, Ruderman D, Macklin P, Rimm DL, Agus DB. Correlating nuclear morphometric patterns with estrogen receptor status in breast cancer pathologic specimens. NPJ Breast Cancer. 2018;4:32. PubMed PMC

Ruffinatti FA, Genova T, Mussano F, Munaron L. MORPHEUS: an automated tool for unbiased and reproducible cell morphometry. J. Cell. Physiol. 2020;235:10110–10115. PubMed

Zimmermann M, et al. Deep learning-based molecular morphometrics for kidney biopsies. JCI Insight. 2021;6:e144779. PubMed PMC

Bouteldja N, et al. Deep learning-based segmentation and quantification in experimental kidney histopathology. J. Am. Soc. Nephrol. 2021;32:52–68. PubMed PMC

Helal I, Fick-Brosnahan GM, Reed-Gitomer B, Schrier RW. Glomerular hyperfiltration: definitions, mechanisms and clinical implications. Nat. Rev. Nephrol. 2012;8:293–300. PubMed

Bülow RD, Dimitrov D, Boor P, Saez-Rodriguez J. How will artificial intelligence and bioinformatics change our understanding of IgA Nephropathy in the next decade? Semin. Immunopathol. 2021;43:739–752. PubMed PMC

Chung H, et al. Joint single-cell measurements of nuclear proteins and RNA in vivo. Nat. Methods. 2021;18:1204–1212. PubMed PMC

Ståhl PL, et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science. 2016;353:78–82. PubMed

Eng C-HL, et al. Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH. Nature. 2019;568:235–239. PubMed PMC

Fogo A, et al. Glomerular hypertrophy in minimal change disease predicts subsequent progression to focal glomerular sclerosis. Kidney Int. 1990;38:115–123. PubMed

Coppo R, et al. Validation of the Oxford classification of IgA nephropathy in cohorts with different presentations and treatments. Kidney Int. 2014;86:828–836. PubMed PMC

Hermsen M, et al. Deep learning-based histopathologic assessment of kidney tissue. J. Am. Soc. Nephrol. 2019;30:1968–1979. PubMed PMC

Kleppe A, et al. Chromatin organisation and cancer prognosis: a pan-cancer study. Lancet Oncol. 2018;19:356–369. PubMed PMC

Taylor-Weiner A, et al. A machine learning approach enables quantitative measurement of liver histology and disease monitoring in NASH. Hepatology. 2021;74:133–147. PubMed PMC

Yi Z, et al. Deep learning identified pathological abnormalities predictive of graft loss in kidney transplant biopsies. Kidney Int. 2022;101:288–298. PubMed PMC

Hermsen M, et al. Quantitative assessment of inflammatory infiltrates in kidney transplant biopsies using multiplex tyramide signal amplification and deep learning. Lab. Invest. 2021;101:970–982. PubMed PMC

Hermsen M, et al. Convolutional neural networks for the evaluation of chronic and inflammatory lesions in kidney transplant biopsies. Am. J. Pathol. 2022;192:1418–1432. PubMed

Rajpurkar P, Chen E, Banerjee O, Topol EJ. AI in health and medicine. Nat. Med. 2022;28:31–38. PubMed

Lu MY, et al. AI-based pathology predicts origins for cancers of unknown primary. Nature. 2021;594:106–110. PubMed

Kers J, et al. Deep learning-based classification of kidney transplant pathology: a retrospective, multicentre, proof-of-concept study. Lancet Digit Health. 2022;4:e18–e26. PubMed

bigpicture. A Central Repository Of Digital Pathology Slides To Boost The Development Of Artificial Intelligence. https://bigpicture.eu/.

Litjens G, Ciompi F, van der Laak J. A decade of gigascience: the challenges of gigapixel pathology images. Gigascience. 2022;11:giac056. PubMed PMC

Jayapandian CP, et al. Development and evaluation of deep learning-based segmentation of histologic structures in the kidney cortex with multiple histologic stains. Kidney Int. 2021;99:86–101. PubMed PMC

de Boer IH, et al. Rationale and design of the Kidney Precision Medicine Project. Kidney Int. 2021;99:498–510. PubMed PMC

HuBMAP Consortium. The human body at cellular resolution: the NIH Human Biomolecular Atlas Program. Nature. 2019;574:187–192. PubMed PMC

Levey AS, et al. A new equation to estimate glomerular filtration rate. Ann. Intern. Med. 2009;150:604–612. PubMed PMC

Bankhead P, et al. QuPath: open source software for digital pathology image analysis. Sci. Rep. 2017;7:16878. PubMed PMC

Isensee F, Jaeger PF, Kohl SAA, Petersen J, Maier-Hein K. H. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods. 2021;18:203–211. PubMed

Ronneberger, O., Fischer, P. & Brox, T. Medical Image Computing and Computer-Assisted Intervention—MICCAI 2015 234–241 (Springer International Publishing, 2015).

Liu, L. et al. On the variance of the adaptive learning rate and beyond. arXiv (2019).

Bouteldja N, et al. Tackling stain variability using CycleGAN-based stain augmentation. J. Pathol. Inform. 2022;13:100140. PubMed PMC

Mingqiang, Y., Kidiyo, K. & Joseph, R. Pattern Recognition Techniques, Technology and Applications (InTech, 2008).

Hothorn, T. & Lausen, B. Maximally selected rank statistics in R. R News (2002).

Hao Y, et al. Integrated analysis of multimodal single-cell data. Cell. 2021;184:3573–3587.e29. PubMed PMC

Hsu, L. L. & Culhane, A. C. corral: Single-cell RNA-seq dimension reduction, batch integration, and visualization with correspondence analysis. bioRxiv10.1101/2021.11.24.469874 (2021). PubMed PMC

Angerer P, et al. destiny: diffusion maps for large-scale single-cell data in R. Bioinformatics. 2016;32:1241–1243. PubMed

Granja JM, et al. ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis. Nat. Genet. 2021;53:403–411. PubMed PMC

Chen WS, et al. Uncovering axes of variation among single-cell cancer specimens. Nat. Methods. 2020;17:302–310. PubMed PMC

Scholz FW, Stephens MA. K-Sample Anderson-Darling tests. J. Am. Stat. Assoc. 1987;82:918–924.

Detection of PatIent-Level distances from single cell genomics and pathomics data with Optimal Transport (PILOT)