背景:糖尿病性黄斑水肿(DME)是糖尿病患者视力丧失的主要原因。本研究旨在开发和评估用于评估DME患者抗血管内皮生长因子(VEGF)治疗反应的OCT组学预测模型。
方法:对82例DME患者113只眼进行回顾性分析。综合特征工程应用于临床和光学相干断层扫描(OCT)数据。Logistic回归,支持向量机(SVM),和反向传播神经网络(BPNN)分类器使用79只眼睛的训练集进行训练,并在34只眼睛的测试集上进行评估。通过决策曲线分析评估OCT组学预测模型的临床意义。性能指标(灵敏度、特异性,F1得分,和AUC)进行计算。
结果:物流,SVM,和BPNN分类器在训练集和测试集中都表现出了鲁棒的判别能力。在训练集中,logistic分类器的敏感性为0.904,特异性为0.741,F1评分为0.887,AUC为0.910.SVM分类器的灵敏度为0.923,特异性为0.667,F1评分为0.881,AUC为0.897。BPNN分类器表现出0.962的灵敏度、0.926的特异性、0.962的F1评分和0.982的AUC。在测试集中保持了类似的辨别能力。非持续性DME组的OCT组学评分明显高于持续性DME组(p<0.001)。OCT组学评分也与治疗后中央亚区厚度下降率呈正相关(Pearson'sR=0.44,p<0.001)。
结论:开发的OCT组学模型准确评估了DME患者的抗VEGF治疗反应。该模型的强大性能和临床意义突出了其作为个性化治疗预测和视网膜病理评估的非侵入性工具的实用性。
BACKGROUND: Diabetic macular edema (DME) is a leading cause of vision loss in patients with diabetes. This study aimed to develop and evaluate an OCT-omics prediction model for assessing anti-vascular endothelial growth factor (VEGF) treatment response in patients with DME.
METHODS: A retrospective analysis of 113 eyes from 82 patients with DME was conducted. Comprehensive feature engineering was applied to clinical and optical coherence tomography (OCT) data. Logistic regression, support vector machine (SVM), and backpropagation neural network (BPNN) classifiers were trained using a training set of 79 eyes, and evaluated on a test set of 34 eyes. Clinical implications of the OCT-omics prediction model were assessed by decision curve analysis. Performance metrics (sensitivity, specificity, F1 score, and AUC) were calculated.
RESULTS: The logistic, SVM, and BPNN classifiers demonstrated robust discriminative abilities in both the training and test sets. In the training set, the logistic classifier achieved a sensitivity of 0.904, specificity of 0.741, F1 score of 0.887, and AUC of 0.910. The SVM classifier showed a sensitivity of 0.923, specificity of 0.667, F1 score of 0.881, and AUC of 0.897. The BPNN classifier exhibited a sensitivity of 0.962, specificity of 0.926, F1 score of 0.962, and AUC of 0.982. Similar discriminative capabilities were maintained in the test set. The OCT-omics scores were significantly higher in the non-persistent DME group than in the persistent DME group (p < 0.001). OCT-omics scores were also positively correlated with the rate of decline in central subfield thickness after treatment (Pearson\'s R = 0.44, p < 0.001).
CONCLUSIONS: The developed OCT-omics model accurately assesses anti-VEGF treatment response in DME patients. The model\'s robust performance and clinical implications highlight its utility as a non-invasive tool for personalized treatment prediction and retinal pathology assessment.