[Clinical Features, Prognostic Analysis and Predictive Model Construction of Central Nervous System Invasion in Peripheral T-Cell Lymphoma]
- Ya-Ting Ma 1, Yan-Fang Chen 1, Zhi-Yuan Zhou 1, Lei Zhang 1, Xin Li 1, Xin-Hua Wang 1, Xiao-Rui Fu 1, Zhen-Chang Sun 1, Yu Chang 1, Fei-Fei Nan 1, Ling Li 1, Ming-Zhi Zhang 1
- Ya-Ting Ma 1, Yan-Fang Chen 1, Zhi-Yuan Zhou 1
- 1Department of Oncology, The First Affiliated Hospital of Zhengzhou University, Lymphoma Diagnosis and Treatment Center of Henan Province, Zhengzhou 450052, Henan Province, China.
- 0Department of Oncology, The First Affiliated Hospital of Zhengzhou University, Lymphoma Diagnosis and Treatment Center of Henan Province, Zhengzhou 450052, Henan Province, China.
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View abstract on PubMed
Summary
This summary is machine-generated.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.
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