ArticleAmerican journal of hematology2025
International Consensus Histopathological Criteria for Subtyping Idiopathic Multicentric Castleman Disease Based on Machine Learning Analysis.
Article in American journal of hematology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Deciphering the full spectrum of Castleman diseases based on a cohort of 700 patients in a western country.British journal of haematology · 2026Article
- Interfollicular Plasmacytosis and Hyperplastic Germinal Centers in Idiopathic Multicentric Castleman Disease, Idiopathic Plasmacytic Lymphadenopathy Subtype.American journal of hematology · 2026Article
- Distinct interleukin-6 production in IPL and TAFRO subtypes of idiopathic multicentric Castleman disease.Haematologica · 2026Article
- Comprehensive analysis of subtype-specific outcomes and management in Castleman disease: a 20-year cohort study.Blood advances · 2026Article
- Limited discriminatory performance of the iMCD-IPI in a Western cohort.The oncologist · 2026Article
- International Consensus Histopathological Criteria for Subtyping Idiopathic Multicentric Castleman Disease Based on Machine Learning Analysis.American journal of hematology · 2025Article
- Morphological and quantitative CT features of anterior mediastinal lesions in TAFRO syndrome and idiopathic multicentric Castleman disease.Frontiers in immunology · 2025Article
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Authors and funding
15 authors.
Funding
Abstract
Idiopathic multicentric Castleman disease (iMCD) is a rare lymphoproliferative disorder classified into three recognized clinical subtypes-idiopathic plasmacytic lymphadenopathy (IPL), TAFRO, and NOS. Although clinical criteria are available for subtyping, diagnostically challenging cases with overlapping histopathological features highlight the need for an improved classification system integrating clinical and histopathological findings. We aimed to develop an objective histopathological subtyping system for iMCD that closely correlates with the clinical subtypes. Excisional lymph node specimens from 94 Japanese iMCD patients (54 IPL, 28 TAFRO, 12 NOS) were analyzed for five key histopathological parameters: germinal center (GC) status, plasmacytosis, vascularity, hemosiderin deposition, and "whirlpool" vessel formation in GC. Using hierarchical clustering, we visualized subgroups and developed a machine learning-based decision tree to differentiate the clinical subtypes and validated it in an external cohort of 12 patients with iMCD. Hierarchical cluster analysis separated the IPL and TAFRO cases into mutually exclusive clusters, whereas the NOS cases were interspersed between them. Decision tree modeling identified plasmacytosis, vascularity, and whirlpool vessel formation as key features distinguishing IPL from TAFRO, achieving 91% and 92% accuracy in the training and test sets, respectively. External validation correctly classified all IPL and TAFRO cases, confirming the reproducibility of the system. Our histopathological classification system closely aligns with the clinical subtypes, offering a more precise approach to iMCD subtyping. It may enhance diagnostic accuracy, guide clinical decision-making for predicting treatment response in challenging cases, and improve patient selection for future research. Further validation of its versatility and clinical utility is required.
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