■息肉状脉络膜血管病变(PCV)是一种出血性眼底疾病,可导致永久性视力丧失。预测PCV中抗VEGF单一疗法的治疗反应始终具有挑战性。我们旨在进行一项前瞻性多中心研究,以探索和确定预测PCV患者抗VEGF治疗反应的影像学生物标志物。建立预测模型,并进行多中心验证。
■这项前瞻性多中心研究利用了来自全国15个眼科中心的未治疗PCV患者的临床特征和图像来筛查生物标志物,开发模型,并验证其性能。北京协和医院的患者被随机分为训练集和内部验证集。通过单变量建立列线图,LASSO回归,和多元回归分析。来自其他14个中心的患者作为外部测试集。曲线下面积(AUC),灵敏度,特异性,并计算了准确性。利用决策曲线分析(DCA)和临床影响曲线(CIC)来评估其在临床决策中的实用性。
■训练集的眼睛分布,内部验证集,和外部测试组分别为66、31和71。“良好的响应者”表现出更薄的中央凹下脉络膜厚度(SFCT)(230.67±61.96与314.42±88.00μm,p<0.001),下脉络膜血管分布指数(CVI)(0.31±0.08vs.0.36±0.05,p=0.006),脉络膜血管通透性过高(CVH)较少(31.0vs.62.2%,p=0.012),和更多的香烟液体(IRF)(58.6vs.29.7%,p=0.018)。SFCT(OR0.990;95%CI0.981-0.999;p=0.033)和CVI(OR0.844;95%CI0.732-0.971;p=0.018)最终被列为最佳预测生物标志物,并以列线图的形式呈现。该模型显示AUC为0.837(95%CI0.738-0.936),0.891(95%CI0.765-1.000),和0.901(95%CI0.824-0.978)用于预测训练集中的“良好响应者”,内部验证集,和外部测试装置,分别,具有出色的灵敏度,特异性,和实用性。
■较小的SFCT和较低的CVI可以作为成像生物标志物,用于预测PCV患者抗VEGF单药治疗的良好治疗反应。基于这些生物标志物的列线图表现出令人满意的性能。
UNASSIGNED: Polypoidal choroidal vasculopathy (PCV) is a hemorrhagic fundus disease that can lead to permanent vision loss. Predicting the treatment response to anti-VEGF monotherapy in PCV is consistently challenging. We aimed to conduct a prospective multicenter
study to explore and identify the imaging biomarkers for predicting the anti-VEGF treatment response in PCV patients, establish predictive model, and undergo multicenter validation.
UNASSIGNED: This prospective multicenter
study utilized clinical characteristics and images of treatment naïve PCV patients from 15 ophthalmic centers nationwide to screen biomarkers, develop model, and validate its performance. Patients from Peking Union Medical College Hospital were randomly divided into a training set and an internal validation set. A nomogram was established by univariate, LASSO regression, and multivariate regression analysis. Patients from the other 14 centers served as an external test set. Area under the curve (AUC), sensitivity, specificity, and accuracy were calculated. Decision curve analysis (DCA) and clinical impact curve (CIC) were utilized to evaluate the practical utility in clinical decision-making.
UNASSIGNED: The eye distribution for the training set, internal validation set, and external test set were 66, 31, and 71, respectively. The \'Good responder\' exhibited a thinner subfoveal choroidal thickness (SFCT) (230.67 ± 61.96 vs. 314.42 ± 88.00 μm, p < 0.001), lower choroidal vascularity index (CVI) (0.31 ± 0.08 vs. 0.36 ± 0.05, p = 0.006), fewer choroidal vascular hyperpermeability (CVH) (31.0 vs. 62.2%, p = 0.012), and more intraretinal fluid (IRF) (58.6 vs. 29.7%, p = 0.018). SFCT (OR 0.990; 95% CI 0.981-0.999; p = 0.033) and CVI (OR 0.844; 95% CI 0.732-0.971; p = 0.018) were ultimately included as the optimal predictive biomarkers and presented in the form of a nomogram. The model demonstrated AUC of 0.837 (95% CI 0.738-0.936), 0.891 (95% CI 0.765-1.000), and 0.901 (95% CI 0.824-0.978) for predicting \'Good responder\' in the training set, internal validation set, and external test set, respectively, with excellent sensitivity, specificity, and practical utility.
UNASSIGNED: Thinner SFCT and lower CVI can serve as imaging biomarkers for predicting good treatment response to anti-VEGF monotherapy in PCV patients. The nomogram based on these biomarkers exhibited satisfactory performances.