AI-Enhanced Quantitative IHC Analysis for Prognostic Stratification in Marginal Zone Lymphoma: Development of a Revised MZL-IPI Model

Objectives: Marginal zone lymphoma (MZL) is a heterogeneous indolent B-cell lymphoma, and current clinical prognostic systems remain limited in identifying transformation risk and refining risk stratification. This study aimed to evaluate the prognostic relevance of artificial intelligence (AI)-quantified immunohistochemical (IHC) markers in MZL and to explore a revised MZL-IPI model. Methods: We retrospectively analyzed 146 patients with pathologically confirmed MZL treated at the Second Xiangya Hospital of Central South University from January 2015 to June 2022. Among them, 111 patients had digitized IHC slides available for AI-assisted quantitative analysis. AI-quantified IHC marker expression was assessed in relation to clinical features, histologic transformation, and survival outcomes. Prognosis-related markers were dichotomized using optimal cut-off values. Survival differences were evaluated using the log-rank test, independent prognostic factors were identified by multivariable Cox regression, and model performance was assessed by cross-validation. Results: Age, B symptoms, hypertension, diabetes mellitus, and gastric involvement were associated with selected IHC parameters. CD3 was independently associated with histologic transformation, with expression below 25.60% indicating higher transformation risk. High CD21 expression independently predicted favorable overall survival (OS), whereas high CD3 expression was associated with inferior progression-free survival (PFS). Incorporating CD21 into the MZL International Prognostic Index (MZL-IPI) improved OS prediction in this cohort. Conclusions: AI-assisted quantitative IHC analysis may provide complementary prognostic information in MZL. The CD21-revised MZL-IPI represents an exploratory framework for integrating AI-derived tissue biomarkers with clinical risk stratification, but external multicenter validation is required before clinical application.