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Machine-Learning-Quantified Lupus Nephritis Histological Features Correlate with NIH Activity and Chronicity Index Subscores

ASN 2022

Study Background

Lupus nephritis (LN) is the most common cause of morbidity and mortality in patients with systemic lupus erythematosus (SLE).1,2 Between 10%-30% of patients with progressive LN will require kidney replacement therapy, due to kidney failure, within 5 years of diagnosis.1,2 The National Institutes of Health (NIH) LN activity and chronicity indices, which are based on histological evaluation and scoring of renal biopsies, are used for diagnosis, to confirm the extent of disease, and inform treatment decisions.1,2,3 However,
interobserver variability and poor quantitation limit the utility of histology-based metrics for precision medicine.2 To mitigate these challenges, and to develop new insight into LN pathobiology, machine learning
(ML)-based models that quantify histologic features in LN were co-developed by Genentech and PathAI.


1.Yu F, Haas M, Glassock R, Zhao MH. Redefining lupus nephritis: clinical
implications of pathophysiologic subtypes. Nat Rev Nephrol. 2017
2.Parikh SV, Almaani S, Brodsky S, Rovin BH. Update on Lupus Nephritis:
Core Curriculum 2020. Am J Kidney Dis. 2020 Aug;76(2):265-281.
3.Bajema IM, Wilhelmus S, Alpers CE, Bruijn JA, Colvin RB, Cook HT, D'Agati
VD, Ferrario F, Haas M, Jennette JC, Joh K, Nast CC, Noël LH, Rijnink EC,
Roberts ISD, Seshan SV, Sethi S, Fogo AB. Revision of the International
Society of Nephrology/Renal Pathology Society classification for lupus
nephritis: clarification of definitions, and modified National Institutes of
Health activity and chronicity indices. Kidney Int. 2018 Apr;93(4):789-796..
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Moll et al.