An automated histological classification system for precision diagnostics of kidney allografts

 An automated histological classification system for precision diagnostics of kidney allografts

Development of the comprehensive Banff Automation System for kidney allograft precision diagnostics.

Alexandre Loupy (Université Paris Cité) and his team, in their latest study published in Nature Medicine, demonstrated the potential of an automated histological classification to improve transplant patient care by correcting diagnostic errors and standardizing allograft rejection diagnoses.

For three decades, the international Banff classification has been the gold standard for kidney allograft rejection diagnosis, but this system has become complex over time with the integration of multimodal data and rules, leading to misclassifications that can have deleterious therapeutic consequences for patients.

To improve diagnosis, Dr. Loupy and colleagues developed a decision-support system, based on an algorithm covering all classification rules and diagnostic scenarios, that automatically assigns kidney allograft diagnoses. They then tested its ability to reclassify rejection diagnoses for adult and pediatric kidney transplant recipients in three international multicentric cohorts and two large prospective clinical trials, including 4,409 biopsies from 3,054 patients (62.05% male and 37.95% female) followed in 20 transplant referral centers in Europe and North America.

In the adult kidney transplant population, the Banff Automation System reclassified 29.75% antibody-mediated rejection cases and 54.29% T cell-mediated rejection cases, whereas 7.32% biopsies diagnosed as non-rejection by pathologists were reclassified as rejection.

In the pediatric population, the reclassification rates were 30.77% for antibody-mediated rejection and 30.77% for T cell-mediated rejection.

The researchers found that reclassification of the initial diagnoses by the Banff Automation System was associated with an improved risk stratification of long-term allograft outcomes.

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Article DOI.

Image Credits: Yoo et al.  Nature Medicine (2023)

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