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A Systematic Review of Erdheim-Chester Disease and IgG4-Related Disease: Building a Diagnostic Framework for the
Simhachalam Gurugubelli1, Rithik Naik Korra2, Venkata Sai Abhilash Meda3
1Department of Internal Medicine, Indiana University School of Medicine, Indianapolis, IN.
Introduction:
Erdheim-Chester disease (ECD) and immunoglobulin G4-related disease (IgG4-RD) are both rare, multisystem disorders with overlapping clinical, radiologic, and histopathologic features. This overlap leads to delays or misdiagnoses. Early diagnosis with proper distinction is critical for treatment and a better prognosis.
Methods:
A review of the literature using PubMed and Google Scholar was conducted to identify distinguishing features between ECD and IgG4-RD. The focus was on clinical presentation, diagnostic imaging, histopathologic findings, immunohistochemistry, and genetic mutations. Relevant articles were screened, and data were synthesized to create a diagnostic framework.
Results:
Both disorders can be characterized by retroperitoneal fibrosis, central nervous system involvement, and IgG4-positive plasma cell infiltration. ECD is characterized by long bone osteosclerosis, "hairy kidneys," a coated aorta, and diabetes insipidus, and is associated with BRAF V600E or MAPK pathway mutations. In contrast, IgG4-RD is characterized by autoimmune pancreatitis, sialadenitis, and increased serum IgG4, as well as storiform fibrosis and obliterative phlebitis on histology. Imaging is key: FDG-PET and MRI frequently show skeletal and cerebellar uptake in ECD, which is typically absent in IgG4-RD. Therapeutically, ECD often requires targeted therapies such as BRAF or MEK inhibitors, whereas IgG4-RD responds to corticosteroids or rituximab or inebilizumab. Treatment response emerged as a new diagnostic clue.
Conclusions:
This review provides a structured, multimodal approach to distinguish ECD from IgG4-RD, which improves diagnostic accuracy by integrating genetic testing, advanced imaging, neurological features, and treatment response. This allows clinicians to avoid misdiagnoses, implement targeted treatments, and improve patient outcomes.
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