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Unicentric Castleman disease: multidetector computed tomography classification with surgical and pathologic
Xiaoli Sun1, Ye Du2, Ying Zhang3
1Department of Radiology, Beijing Shijitan Hospital, Peking University Ninth School of Clinical Medicine, Capital Medical University, Beijing, China.
Insights
Multidetector computed tomography (MDCT) classification of unicentric Castleman disease correlates with surgical and pathologic findings. This imaging-based classification aids in evaluating tumor infiltration and optimizing surgical planning for better patient outcomes.
Area of Science:
- Radiology
- Oncology
- Pathology
Background:
- Unicentric Castleman disease (UCD) is a rare lymphoproliferative disorder.
- Accurate preoperative assessment of UCD is crucial for surgical planning and patient management.
Purpose of the Study:
- To correlate multidetector computed tomography (MDCT) classification of UCD with its surgical and pathologic features.
- To evaluate the utility of MDCT in assessing tumor characteristics and guiding surgical intervention.
Main Methods:
- Retrospective analysis of 63 UCD cases confirmed by pathology.
- All patients underwent MDCT examinations.
- Radiologists classified UCD based on imaging manifestations and reviewed surgical, histopathological, and imaging features.
Main Results:
- UCD cases were classified into four imaging types (I-IV) based on mass characteristics and margins.
- Types I and II (smooth or irregular margins) were predominantly hyaline-vascular type post-resection.
- Type III (invasive, blurred margin) showed varied resection success and mixed pathology, while Type IV (multiple fused masses) had incomplete resection in one case (plasma cell type).
Conclusions:
- MDCT is highly effective for detecting and diagnosing UCD.
- MDCT-based classification reliably assesses tumor infiltration and growth patterns.
- This classification system assists in optimizing surgical strategies for UCD.
Background:
This study aimed to correlate multidetector computed tomography (MDCT) classification of unicentric Castleman disease with the surgical and pathologic features.
Methods:
The imaging manifestations of 63 cases of unicentric Castleman disease confirmed by pathology were retrospectively analyzed. Every patient underwent an MDCT examination. Classification based on imaging manifestations, surgical, histopathological, and imaging features were simultaneously reviewed and analyzed by two radiologists, with any disagreements resolved by consensus.
Results:
Sixty-three patients with unicentric Castleman disease were divided into I-IV types by imaging manifestations: type I, single mass with smooth margin (n=5); type II, single mass with irregular or lobulated margin (n=33); type III, single invasive mass with blurred margin (n=20); and type IV, multiple fused masses (n=5). Thirty-eight cases of type I and type II were diagnosed as hyaline-vascular type by pathology after complete surgical resection; 20 cases were type III, in which eight cases were partially resected, 17 cases were pathologically diagnosed as hyaline-vascular type, and the remaining three cases were a mixed type. In five cases of type IV that could not be completely resected, four cases were hyaline-vascular type, and one case was plasma cell type.
Conclusions:
MDCT is an excellent tool for the detection and diagnosis of unicentric Castleman disease. Classification by MDCT features is a reliable method for evaluating tumor infiltration and growth mode, which helps surgical optimization.
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