Digital Pathology and FSGS Subtype Classification - EMJ

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Digital Pathology Improves Glomerulosclerosis Classification

Key Summary:

  • Digital pathology was evaluated as a tool for distinguishing primary and maladaptive FSGS.
  • Mean interglomerular area helped differentiate FSGS subtypes across two patient cohorts.
  • Combining digital pathology with serum albumin could improve FSGS classification in future studies.

DIGITAL pathology could improve the classification of focal and segmental glomerulosclerosis (FSGS), with new research identifying mean interglomerular area (MIA) as a useful marker for distinguishing primary FSGS (pFSGS) from maladaptive FSGS (mFSGS). Combining this histological measurement with serum albumin levels could help address diagnostic uncertainty and support future research into disease mechanisms and treatment approaches.

Improving FSGS Classification Through Digital Pathology

FSGS encompasses several disease subtypes associated with different pathophysiological mechanisms and therapeutic approaches. While pFSGS is associated with immunological factors and typically treated with immunosuppressants, conventional histological examination using light microscopy cannot reliably establish the underlying disease aetiology.

To investigate whether digital pathology could improve subtype classification, researchers conducted a retrospective, observational, multicentre study involving three university hospitals in Paris and Bordeaux.

The investigation included two patient cohorts. An ultra-phenotyped cohort comprised 58 patients with established pFSGS or mFSGS, while a separate clinical cohort included 89 patients with non-selected FSGS, encompassing primary, maladaptive, and undetermined subtypes.

Mean Interglomerular Area Distinguished Disease Subtypes

The researchers initially assessed whether histological morphometric measurements could distinguish between pFSGS and mFSGS. Multivariate analysis identified MIA as a marker associated with disease subtype.

Higher MIA values were associated with mFSGS, while lower measurements were observed in patients with pFSGS. This distinction remained evident following stratification by age, eGFR, and proteinuria in the ultra-phenotyped cohort.

The researchers subsequently evaluated MIA in the clinical cohort, where measurements were also significantly lower among patients with pFSGS.

Although MIA demonstrated a strong correlation with serum albumin, a conventional marker associated with pFSGS, the two measures produced conflicting classifications in 30% of patients. Within this subgroup, relying exclusively on serum albumin frequently resulted in incorrect subtype classification, affecting 27% of patients.

Combining Biomarkers Could Support Clinical Research

These findings highlighted the potential value of combining digital pathology measurements with established biological parameters when distinguishing FSGS subtypes.

Importantly, patients with FSGS of uncertain origin were distributed alongside either pFSGS or mFSGS cases when MIA and serum albumin were assessed together. This suggested that undetermined FSGS may not represent a distinct pathological entity.

The researchers concluded that digital pathology, alongside readily available biological measurements, could provide a useful approach to FSGS classification.

Although further investigation is needed to establish its clinical application, combining MIA with serum albumin may help improve subtype identification. This approach could also support the development of more accurately characterised patient groups for future therapeutic and mechanistic FSGS studies.

Reference

Le Moal P et al. Digital pathology approach for the classification of focal and segmental glomerulosclerosis. Sci Rep. 2026;DOI:10.1038/s41598-026-77202-4.

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