Distinguishing lupus lymphadenitis from Kikuchi disease based on clinicopathological features and C4d immunohistochemistry

doi:10.1093/rheumatology/keaa524

中山醫學大學機構典藏 CSMUIR > 中山醫學大學研究成果 > 期刊論文 > Item 310902500/23452

Please use this identifier to cite or link to this item: https://ir.csmu.edu.tw:8080/ir/handle/310902500/23452

Title:	Distinguishing lupus lymphadenitis from Kikuchi disease based on clinicopathological features and C4d immunohistochemistry
Authors:	Yu, SC;Chang, KC;Wang, H;Li, MF;Yang, TL;Chen, CN;Chen, CJ;Chen, KC;Shen, CY;Kuo, PY;Lin, LW;Lin, YM;Lin, WC
Keywords:	artificial intelligence;biopsy;C4d;histopathology;immunohistochemistry;Kikuchi-Fujimoto disease;lymphadenopathy;machine learning;necrotizing lymphadenitis;systemic lupus erythematosus
Date:	2021
Issue Date:	2022-08-05T09:37:31Z (UTC)
Publisher:	OXFORD UNIV PRESS
ISSN:	1462-0324
Abstract:	Objectives. Distinguishing Kikuchi disease (KD) from lupus lymphadenitis (LL) histologically is nearly impossible. We applied C4d immunohistochemical (IHC) stain to develop diagnostic tools. Methods. We retrospectively investigated clinicopathological features and C4d IHC staining in an LL-enriched development cohort (19 LL and 81 KD specimens), proposed risk stratification criteria and trained machine learning models, and validated them in an external cohort (2 LL and 55 KD specimens). Results. Clinically, we observed that LL was associated with an older average age (33 vs 25 years; P=0.005), higher proportion of biopsy sites other than the neck [4/19 (21%) vs 1/81 (1%); P=0.004], and higher proportion of generalized lymphadenopathy compared with KD [9/16 (56%) vs 7/31 (23%); P=0.028]. Histologically, LL involved a larger tissue area than KD did (P=0.006). LL specimens exhibited more frequent interfollicular pattern [5/19 (26%) vs 3/81 (4%); P=0.001] and plasma cell infiltrates (P=0.002), and less frequent histiocytic infiltrates in the necrotic area (P=0.030). Xanthomatous infiltrates were noted in 6/19 (32%) LL specimens. Immunohistochemically, C4d endothelial staining in the necrotic area [11/17 (65%) vs 2/62 (3%); P<10(-7)], and capillaries/venules [5/19 (26%) vs 7/81 (9%); P=0.048] and trabecular/hilar vessels [11/18 (61%) vs 8/81 (10%); P<10(-4)] in the viable area was more common in LL. During validation, both the risk stratification criteria and machine learning models were superior to conventional histological criteria. Conclusions. Integrating clinicopathological and C4d findings could distinguish LL from KD.
URI:	http://dx.doi.org/10.1093/rheumatology/keaa524 https://www.webofscience.com/wos/woscc/full-record/WOS:000637045600078 https://ir.csmu.edu.tw:8080/handle/310902500/23452
Relation:	RHEUMATOLOGY ,2021,v60,issue 3, P1543-1552
Appears in Collections:	[中山醫學大學研究成果] 期刊論文

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