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Distinguishing lupus lymphadenitis from Kikuchi disease based on clinicopathological features and C4d
Shan-Chi Yu1,2,3, Kung-Chao Chang4, Hsuan Wang5
1Department of Pathology and Graduate Institute of Pathology, College of Medicine, National Taiwan University, Taipei, Taiwan.
Insights
Distinguishing Kikuchi disease (KD) from lupus lymphadenitis (LL) is challenging. C4d immunohistochemistry combined with clinicopathological features and machine learning models can accurately differentiate these conditions, improving diagnostic accuracy.
Area of Science:
- Immunopathology
- Diagnostic Histology
- Machine Learning in Medicine
Background:
- Histological differentiation between Kikuchi disease (KD) and lupus lymphadenitis (LL) is difficult.
- Novel diagnostic markers are needed to distinguish these two conditions accurately.
Purpose of the Study:
- To develop and validate diagnostic tools for distinguishing KD from LL using C4d immunohistochemistry (IHC).
- To integrate clinicopathological features with C4d IHC findings for improved diagnostic accuracy.
Main Methods:
- Retrospective analysis of clinicopathological features and C4d IHC staining in development (19 LL, 81 KD) and validation (2 LL, 55 KD) cohorts.
- Development of risk stratification criteria and machine learning models for LL vs. KD differentiation.
- Comparison of proposed methods with conventional histological criteria.
Main Results:
- LL cases showed distinct clinical features including older age, varied biopsy sites, and generalized lymphadenopathy compared to KD.
- Histological analysis revealed differences in tissue area, inflammatory patterns, and infiltrates between LL and KD.
- C4d endothelial staining in necrotic areas and vessels in viable areas was significantly more frequent in LL.
- Validated risk stratification criteria and machine learning models outperformed conventional histological methods.
Conclusions:
- Integration of clinicopathological data and C4d IHC findings provides a robust method for distinguishing LL from KD.
- The developed tools, including risk stratification and machine learning models, enhance diagnostic precision for these challenging conditions.
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.
