Nishimura Midori Filiz, Haratake Tomoka, Nishimura Yoshito, Nishikori Asami, Sumiyoshi Remi, Ujiie Hideki, Kawahara Yuri, Koga Tomohiro, Ueki Masao, Laczko Dorottya, Oksenhendler Eric, Fajgenbaum David C, van Rhee Frits, Kawakami Atsushi, Sato Yasuharu
Department of Molecular Hematopathology, Okayama University Graduate School of Health Sciences, Okayama, Japan.
The Research Program for Intractable Disease by Ministry of Health, Labor and Welfare, Castleman Disease, TAFRO and Related Ddisease Research Group, Nagasaki, Japan.
Am J Hematol. 2025 Jun 20. doi: 10.1002/ajh.27743.
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.
特发性多中心Castleman病(iMCD)是一种罕见的淋巴增殖性疾病,分为三种公认的临床亚型——特发性浆细胞性淋巴结病(IPL)、TAFRO和未分类(NOS)。尽管有临床标准用于亚型分类,但具有重叠组织病理学特征的诊断挑战性病例凸显了整合临床和组织病理学结果的改进分类系统的必要性。我们旨在开发一种与临床亚型密切相关的iMCD客观组织病理学亚型分类系统。对94例日本iMCD患者(54例IPL、28例TAFRO、12例NOS)的切除淋巴结标本分析了五个关键组织病理学参数:生发中心(GC)状态、浆细胞增多、血管形成、含铁血黄素沉积以及GC中的“漩涡状”血管形成。使用层次聚类,我们可视化了亚组并开发了基于机器学习的决策树以区分临床亚型,并在12例iMCD患者的外部队列中进行了验证。层次聚类分析将IPL和TAFRO病例分为相互排斥的簇,而NOS病例则散布在它们之间。决策树建模确定浆细胞增多、血管形成和漩涡状血管形成是区分IPL与TAFRO的关键特征,在训练集和测试集中的准确率分别达到91%和92%。外部验证正确分类了所有IPL和TAFRO病例,证实了该系统的可重复性。我们的组织病理学分类系统与临床亚型紧密相关,为iMCD亚型分类提供了更精确的方法。它可能提高诊断准确性,指导临床决策以预测具有挑战性病例的治疗反应,并改善未来研究的患者选择。需要进一步验证其通用性和临床实用性。