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Machine Learning for Monoclonal Gammopathies of Renal Significance Risk Stratification Using Clinical and Pathology

Giorgio Cazzaniga1, Giulia Capitoli2,3, Raffaella Barretta1

  • 1Department of Medicine and Surgery, Pathology, University of Milano-Bicocca, IRCCS Fondazione San Gerardo dei Tintori, Monza, Italy.

Kidney International Reports
|August 15, 2025
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Summary

A new machine learning tool aids in identifying patients with monoclonal gammopathies of renal significance (MGRS) before a kidney biopsy. This AI-guided evaluation helps prioritize patients for necessary histological examination, improving MGRS diagnosis.

Keywords:
MGRScomputational pathologydigital healthdigital pathologyonco-nephrologyrenal biopsy

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Area of Science:

  • Nephrology
  • Oncology
  • Artificial Intelligence

Background:

  • Prompt detection of monoclonal gammopathies of renal significance (MGRS) is crucial for initiating chemotherapy.
  • Renal biopsy is the gold standard for MGRS diagnosis but is not always performed or may be delayed.
  • Clinical and laboratory data can suggest MGRS but lack definitive diagnostic power.

Purpose of the Study:

  • To develop and validate a machine learning (ML) tool for prebiopsy risk stratification of MGRS.
  • To assist clinicians in identifying patients who would benefit most from a renal biopsy.
  • To reinforce the clinical rationale for histological examination in suspected MGRS cases.

Main Methods:

  • Retrospective study involving 258 patients with monoclonal gammopathy of undetermined significance.
  • Classification of patients into MGRS (168 cases) and non-MGRS (90 cases) based on renal biopsy results.
  • Development and validation of an ML model using clinical and laboratory data.

Main Results:

  • The ML model achieved an accuracy of 0.79 and an AUC of 0.80 in the validation set.
  • MGRS patients were more frequently female and had higher rates of Bence Jones proteinuria.
  • Amyloidosis was the most common MGRS diagnosis (62%).

Conclusions:

  • An ML-based tool can effectively stratify MGRS risk before biopsy.
  • The tool aids in selecting patients with a higher probability of MGRS for histological characterization.
  • The developed ML model is available as a free mobile and desktop application (MIRAGE).