融合眼底结构与视野功能数据，提前预测原发性闭角型青光眼进展速度。
Fusing Structural Phenotypes with Functional Data for Early Prediction of Primary Angle Closure Glaucoma Progression
- 结合眼底结构与视野分区参数，用机器学习分类进展快慢
- 联合模型准确率AUC达0.87，优于仅用结构或功能数据的模型
- 下象限眼底厚度和神经纤维层最能预测进展，适合临床风险评估
目的：通过整合视盘结构特征与分区视野功能参数，对原发性闭角型青光眼（PACG）患者的眼部进展速度进行快/慢分类。方法：纳入299例患者共451只眼，均接受超过5次可靠视野检查且随访时间超5年。进展判断依据Zeiss Forum系统，基线视野在OCT检查前6个月内完成。快速进展定义为视野指数（VFI）年下降速率<-2.0%；缓慢进展则> -2.0%。使用AI分割OCT图像提取31个视盘结构参数，结合视网膜神经纤维层分布划分五个半场区域，将各区域平均敏感度与结构参数联合训练机器学习模型。采用多种模型对比，通过SHAP分析识别关键预测因子。主要指标：利用结构与功能联合数据分类慢/快速进展者。结果：共451只眼，平均VFI年下降0.92%；其中369只缓慢进展，82只快速进展。随机森林模型在结合结构与功能数据时表现最佳（AUC=0.87，经2000次蒙特卡洛迭代验证）。SHAP分析识别出6个关键预测因子：下方平均视网膜神经纤维层厚度（MRW）、下方及下方颞侧视网膜神经纤维层厚度、鼻颞侧视盘玻璃膜疣曲率、上方鼻侧视野敏感度，以及下方视网膜神经节细胞+内颗粒层厚度。仅用结构或功能数据的模型性能较差，对应AUC分别为0.82和0.78。结论：整合视盘结构与视野功能参数显著提升对PACG进展风险的分类能力。下方视盘形态特征，尤其是MRW与神经纤维层厚度，是最重要的预测指标，凸显其在疾病监测中的核心作用。
原文摘要 · Abstract (English)
Purpose: To classify eyes as slow or fast glaucoma progressors in patients with primary angle closure glaucoma (PACG) using an integrated approach combining optic nerve head (ONH) structural features and sector-based visual field (VF) functional parameters. Methods: PACG patients with >5 reliable VF tests over >5 years were included. Progression was assessed in Zeiss Forum, with baseline VF within six months of OCT. Fast progression was VFI decline <-2.0% per year; slow progression >-2.0% per year. OCT volumes were AI-segmented to extract 31 ONH parameters. The Glaucoma Hemifield Test defined five regions per hemifield, aligned with RNFL distribution. Mean sensitivity per region was combined with structural parameters to train ML classifiers. Multiple models were tested, and SHAP identified key predictors. Main outcome measures: Classification of slow versus fast progressors using combined structural and functional data. Results: We analyzed 451 eyes from 299 patients. Mean VFI progression was -0.92% per year; 369 eyes progressed slowly and 82 rapidly. The Random Forest model combining structural and functional features achieved the best performance (AUC = 0.87, 2000 Monte Carlo iterations). SHAP identified six key predictors: inferior MRW, inferior and inferior-temporal RNFL thickness, nasal-temporal LC curvature, superior nasal VF sensitivity, and inferior RNFL and GCL+IPL thickness. Models using only structural or functional features performed worse with AUC of 0.82 and 0.78, respectively. Conclusions: Combining ONH structural and VF functional parameters significantly improves classification of progression risk in PACG. Inferior ONH features, MRW and RNFL thickness, were the most predictive, highlighting the critical role of ONH morphology in monitoring disease progression.
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