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Updated: May 28, 2026
07:52
Multiplexed Fluorescent Immunohistochemical Staining, Imaging, and Analysis in Histological Samples of Lymphoma
Published on: January 9, 2019
AI-Enhanced Quantitative IHC Analysis for Prognostic Stratification in Marginal Zone Lymphoma: Development of a
Qingyang Zhang1, Zeyu Deng1, Wenzhe Yan1
1Department of Hematology, The Second Xiangya Hospital of Central South University, Changsha 410011, China.
Diagnostics (Basel, Switzerland)
|May 27, 2026
Summary
Artificial intelligence (AI) analysis of immunohistochemical (IHC) markers in marginal zone lymphoma (MZL) identified CD21 and CD3 as key prognostic indicators. Integrating these AI-quantified markers into the MZL International Prognostic Index (MZL-IPI) may improve risk stratification for this indolent B-cell lymphoma.
Area of Science:
- Hematology
- Oncology
- Medical Informatics
Background:
- Marginal zone lymphoma (MZL) is a heterogeneous indolent B-cell lymphoma with limited current prognostic systems for risk stratification.
- Identifying transformation risk and refining prognostic models are crucial for optimizing patient management in MZL.
Purpose of the Study:
- To evaluate the prognostic relevance of artificial intelligence (AI)-quantified immunohistochemical (IHC) markers in MZL.
- To explore a revised MZL International Prognostic Index (MZL-IPI) incorporating AI-derived biomarkers.
Main Methods:
- Retrospective analysis of 146 MZL patients, with 111 undergoing AI-assisted quantitative IHC analysis.
- Correlation of AI-quantified IHC marker expression with clinical features, histologic transformation, and survival outcomes.
- Multivariable Cox regression and cross-validation to identify independent prognostic factors and assess model performance.
Main Results:
- CD3 expression below 25.60% was independently associated with higher histologic transformation risk.
- High CD21 expression predicted favorable overall survival (OS), while high CD3 expression predicted inferior progression-free survival (PFS).
- Incorporating CD21 into the MZL-IPI improved OS prediction in the studied cohort.
Conclusions:
- AI-assisted quantitative IHC analysis offers complementary prognostic information for MZL.
- A CD21-revised MZL-IPI framework integrates AI-derived tissue biomarkers for improved clinical risk stratification.
- External multicenter validation is necessary before clinical application of the revised MZL-IPI.
