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. 2022 Sep;17(9):1316-1324.
doi: 10.2215/CJN.01760222. Epub 2022 Jul 26.

Automated Prediction of Kidney Failure in IgA Nephropathy with Deep Learning from Biopsy Images

Francesca Testa  1 , Francesco Fontana  1 , Federico Pollastri  2 , Johanna Chester  3 , Marco Leonelli  1 , Francesco Giaroni  1 , Fabio Gualtieri  3 , Federico Bolelli  2 , Elena Mancini  4 , Maurizio Nordio  5 , Paolo Sacco  6 , Giulia Ligabue  3 , Silvia Giovanella  3 , Maria Ferri  3 , Gaetano Alfano  1 , Loreto Gesualdo  7 , Simonetta Cimino  8 , Gabriele Donati  1   3 , Costantino Grana  2 , Riccardo Magistroni  9   3
Affiliations

Automated Prediction of Kidney Failure in IgA Nephropathy with Deep Learning from Biopsy Images

Francesca Testa et al. Clin J Am Soc Nephrol. 2022 Sep.

Abstract

Background and objectives: Digital pathology and artificial intelligence offer new opportunities for automatic histologic scoring. We applied a deep learning approach to IgA nephropathy biopsy images to develop an automatic histologic prognostic score, assessed against ground truth (kidney failure) among patients with IgA nephropathy who were treated over 39 years. We assessed noninferiority in comparison with the histologic component of currently validated predictive tools. We correlated additional histologic features with our deep learning predictive score to identify potential additional predictive features.

Design, setting, participants, & measurements: Training for deep learning was performed with randomly selected, digitalized, cortical Periodic acid-Schiff-stained sections images (363 kidney biopsy specimens) to develop our deep learning predictive score. We estimated noninferiority using the area under the receiver operating characteristic curve (AUC) in a randomly selected group (95 biopsy specimens) against the gold standard Oxford classification (MEST-C) scores used by the International IgA Nephropathy Prediction Tool and the clinical decision supporting system for estimating the risk of kidney failure in IgA nephropathy. We assessed additional potential predictive histologic features against a subset (20 kidney biopsy specimens) with the strongest and weakest deep learning predictive scores.

Results: We enrolled 442 patients; the 10-year kidney survival was 78%, and the study median follow-up was 6.7 years. Manual MEST-C showed no prognostic relationship for the endocapillary parameter only. The deep learning predictive score was not inferior to MEST-C applied using the International IgA Nephropathy Prediction Tool and the clinical decision supporting system (AUC of 0.84 versus 0.77 and 0.74, respectively) and confirmed a good correlation with the tubolointerstitial score (r=0.41, P<0.01). We observed no correlations between the deep learning prognostic score and the mesangial, endocapillary, segmental sclerosis, and crescent parameters. Additional potential predictive histopathologic features incorporated by the deep learning predictive score included (1) inflammation within areas of interstitial fibrosis and tubular atrophy and (2) hyaline casts.

Conclusions: The deep learning approach was noninferior to manual histopathologic reporting and considered prognostic features not currently included in MEST-C assessment.

Podcast: This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2022_07_26_CJN01760222.mp3.

Keywords: IgA nephropathy; MEST-C; Oxford classification; artificial intelligence; deep learning; histopathology; kidney biopsy; kidney failure; renal insufficiency.

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Figures

None
Graphical abstract
Figure 1.
Figure 1.
Flow chart of kidney biopsy specimen selection, digitalization, and randomization in the training/validation set and test set. *A total of 15 biopsy specimens were provided for the VALIGA study (25); 37 biopsy specimens are missing for other reasons.
Figure 2.
Figure 2.
Workflow of slide segmentation and production of overlapping patches.
Figure 3.
Figure 3.
Receiver operating characteristic curves of prediction accuracy of our deep learning predictive score (DLPS), the International IgA Nephropathy Prediction Tool (IIPT), and the clinical decision supporting system for estimating the risk of kidney failure in IgA nephropathy (CDSS). Analysis of the final test set (n=95 biopsy specimens). AUC, area under the receiver operating characteristic curve.
See this image and copyright information in PMC

References

    1. Cattran DC, Coppo R, Cook HT, Feehally J, Roberts IS, Troyanov S, Alpers CE, Amore A, Barratt J, Berthoux F, Bonsib S, Bruijn JA, D’Agati V, D’Amico G, Emancipator S, Emma F, Ferrario F, Fervenza FC, Florquin S, Fogo A, Geddes CC, Groene HJ, Haas M, Herzenberg AM, Hill PA, Hogg RJ, Hsu SI, Jennette JC, Joh K, Julian BA, Kawamura T, Lai FM, Leung CB, Li LS, Li PK, Liu ZH, Mackinnon B, Mezzano S, Schena FP, Tomino Y, Walker PD, Wang H, Weening JJ, Yoshikawa N, Zhang H; Working Group of the International IgA Nephropathy Network and the Renal Pathology Society : The Oxford classification of IgA nephropathy: Rationale, clinicopathological correlations, and classification. Kidney Int 76: 534–545, 2009 - PubMed
    1. Roberts IS, Cook HT, Troyanov S, Alpers CE, Amore A, Barratt J, Berthoux F, Bonsib S, Bruijn JA, Cattran DC, Coppo R, D’Agati V, D’Amico G, Emancipator S, Emma F, Feehally J, Ferrario F, Fervenza FC, Florquin S, Fogo A, Geddes CC, Groene HJ, Haas M, Herzenberg AM, Hill PA, Hogg RJ, Hsu SI, Jennette JC, Joh K, Julian BA, Kawamura T, Lai FM, Li LS, Li PK, Liu ZH, Mackinnon B, Mezzano S, Schena FP, Tomino Y, Walker PD, Wang H, Weening JJ, Yoshikawa N, Zhang H; Working Group of the International IgA Nephropathy Network and the Renal Pathology Society : The Oxford classification of IgA nephropathy: Pathology definitions, correlations, and reproducibility. Kidney Int 76: 546–556, 2009 - PubMed
    1. Trimarchi H, Barratt J, Cattran DC, Cook HT, Coppo R, Haas M, Liu ZH, Roberts IS, Yuzawa Y, Zhang H, Feehally J; IgAN Classification Working Group of the International IgA Nephropathy Network and the Renal Pathology Society; Conference Participants : Oxford Classification of IgA nephropathy 2016: An update from the IgA Nephropathy Classification Working Group. Kidney Int 91: 1014–1021, 2017 - PubMed
    1. Kidney Disease: Improving Global Outcomes Glomerular Diseases Work Group : KDIGO 2021 Clinical Practice Guideline for the Management of Glomerular Diseases. Kidney Int 100: S1–S276, 2021 - PubMed
    1. Furness PN, Taub N: Interobserver reproducibility and application of the ISN/RPS classification of lupus nephritis-a UK-wide study. Am J Surg Pathol 30: 1030–1035, 2006 - PubMed

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