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Updated: Sep 17, 2025
08:59
A Model for Perineural Invasion in Head and Neck Squamous Cell Carcinoma
Published on: January 5, 2017
10.8K
[Clinical Features, Prognostic Analysis and Predictive Model Construction of Central Nervous System Invasion in
Ya-Ting Ma1, Yan-Fang Chen1, Zhi-Yuan Zhou1
1Department of Oncology, The First Affiliated Hospital of Zhengzhou University, Lymphoma Diagnosis and Treatment Center of Henan Province, Zhengzhou 450052, Henan Province, China.
Zhongguo Shi Yan Xue Ye Xue Za Zhi
|July 4, 2025
Summary
A new model accurately predicts central nervous system (CNS) invasion in peripheral T-cell lymphoma (PTCL). This tool helps identify high-risk patients for earlier intervention, improving outcomes for PTCL with CNS involvement.
Area of Science:
- Hematology
- Oncology
- Neurology
Context:
- Peripheral T-cell lymphoma (PTCL) is a heterogeneous group of aggressive non-Hodgkin lymphomas.
- Central nervous system (CNS) invasion is a rare but serious complication of PTCL, associated with poor prognosis.
- Accurate prediction of CNS invasion is crucial for timely intervention and improved patient outcomes.
Purpose:
- To investigate the clinical features and prognosis of CNS invasion in PTCL.
- To develop and validate a risk prediction model for CNS invasion in PTCL patients.
Summary:
- A retrospective analysis of 395 PTCL patients identified an incidence of 3.3% for CNS invasion.
- Independent risk factors for CNS invasion included extranodal involvement, anaplastic large cell lymphoma (ALCL) subtype, and ECOG performance status >1.
- A novel prediction model stratified patients into low, intermediate, and high-risk groups, with a 1-year cumulative incidence of 50.0% in the high-risk group.
Impact:
- The developed model demonstrates good discrimination and accuracy for predicting CNS invasion in PTCL.
- This tool facilitates precise risk stratification, enabling targeted prophylactic strategies and early therapeutic interventions.
- Further validation with larger datasets is recommended to confirm the model's specificity and sensitivity.
