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International Consensus Histopathological Criteria for Subtyping Idiopathic Multicentric Castleman Disease Based on

Midori Filiz Nishimura1,2, Tomoka Haratake1, Yoshito Nishimura3,4

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Summary

A new histopathological classification system accurately subtypes idiopathic multicentric Castleman disease (iMCD), distinguishing between IPL and TAFRO subtypes. This aids in diagnosis and treatment prediction for this rare lymphoproliferative disorder.

Keywords:
clinical subtypehistopathological criteriaidiopathic multicentric castleman diseaselymphoproliferative diseasemachine‐learning

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Area of Science:

  • Hematology
  • Pathology
  • Oncology

Background:

  • Idiopathic multicentric Castleman disease (iMCD) is a rare lymphoproliferative disorder with recognized clinical subtypes: idiopathic plasmacytic lymphadenopathy (IPL), TAFRO, and NOS.
  • Existing clinical criteria for iMCD subtyping can be challenging due to overlapping histopathological features, necessitating an improved classification system.
  • An integrated approach combining clinical and histopathological findings is crucial for accurate iMCD subtyping.

Purpose of the Study:

  • To develop an objective histopathological subtyping system for iMCD that closely correlates with established clinical subtypes.
  • To create a machine learning-based decision tree for differentiating iMCD subtypes based on key histopathological parameters.
  • To validate the developed system's accuracy and reproducibility in distinguishing between IPL and TAFRO subtypes.

Main Methods:

  • Analysis of excisional lymph node specimens from 94 Japanese iMCD patients (54 IPL, 28 TAFRO, 12 NOS).
  • Evaluation of five key histopathological parameters: germinal center (GC) status, plasmacytosis, vascularity, hemosiderin deposition, and 'whirlpool' vessel formation in GC.
  • Application of hierarchical clustering and a machine learning-based decision tree for subtyping, with validation on an external cohort of 12 iMCD patients.

Main Results:

  • Hierarchical clustering separated IPL and TAFRO cases into distinct clusters, with NOS cases interspersed.
  • Decision tree modeling identified plasmacytosis, vascularity, and whirlpool vessel formation as key differentiators between IPL and TAFRO, achieving 91% (training) and 92% (test) accuracy.
  • External validation correctly classified all IPL and TAFRO cases, confirming the system's reproducibility.

Conclusions:

  • The developed histopathological classification system accurately aligns with clinical iMCD subtypes, offering a precise diagnostic approach.
  • This system can enhance diagnostic accuracy, guide clinical decision-making for treatment response prediction, and aid in patient selection for research.
  • Further validation is required to confirm the versatility and clinical utility of this novel iMCD subtyping system.