用于边缘区淋巴瘤预后分层的AI增强定量IHC分析：修订的MZL-IPI模型的开发
AI-Enhanced Quantitative IHC Analysis for Prognostic Stratification in Marginal Zone Lymphoma: Development of a Revised MZL-IPI Model.
摘要(中文译文)
边缘区淋巴瘤（MZL）是一种异质性惰性B细胞淋巴瘤，现有临床预后系统在识别转化风险和细化风险分层方面仍存在局限。本研究旨在评估人工智能（AI）量化的免疫组化（IHC）标志物在MZL中的预后相关性，并探索修订的MZL-IPI模型。我们回顾性分析了2015年1月至2022年6月在中南大学湘雅二医院接受治疗的146例经病理确诊的MZL患者。其中，111例患者有可用于AI辅助定量分析的数字化IHC切片。评估了AI量化的IHC标志物表达与临床特征、组织学转化及生存结局的关系。采用最佳截断值将预后相关标志物二分类。采用log-rank检验评估生存差异，通过多因素Cox回归确定独立预后因素，并通过交叉验证评估模型性能。年龄、B症状、高血压、糖尿病和胃受累与部分IHC参数相关。CD3与组织学转化独立相关，表达低于25.60%提示转化风险较高。高CD21表达独立预测较好的总生存（OS），而高CD3表达与较差的无进展生存（PFS）相关。将CD21纳入MZL国际预后指数（MZL-IPI）改善了该队列的OS预测。AI辅助定量IHC分析可能为MZL提供补充性预后信息。CD21修订的MZL-IPI是将AI衍生的组织生物标志物与临床风险分层相结合的探索性框架，但在临床应用前尚需外部多中心验证。
Abstract
: 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. : 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. : 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. : 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.
如何引用
AMA
Qingyang Zhang, Zeyu Deng, Wenzhe Yan, Hongkai Zhu, Zizhu Tian, Yi Jiang, et al. AI-Enhanced Quantitative IHC Analysis for Prognostic Stratification in Marginal Zone Lymphoma: Development of a Revised MZL-IPI Model.. Diagnostics (Basel, Switzerland). 2026; doi:10.3390/diagnostics16101456.
APA
Qingyang Zhang, Zeyu Deng, Wenzhe Yan, Hongkai Zhu, Zizhu Tian, Yi Jiang, et al (2026). AI-Enhanced Quantitative IHC Analysis for Prognostic Stratification in Marginal Zone Lymphoma: Development of a Revised MZL-IPI Model.. Diagnostics (Basel, Switzerland). https://doi.org/10.3390/diagnostics16101456
GB/T 7714
Qingyang Zhang, Zeyu Deng, Wenzhe Yan, Hongkai Zhu, Zizhu Tian, Yi Jiang, et al. AI-Enhanced Quantitative IHC Analysis for Prognostic Stratification in Marginal Zone Lymphoma: Development of a Revised MZL-IPI Model.[J]. Diagnostics (Basel, Switzerland), 2026 doi:10.3390/diagnostics16101456.
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