Multi-Compartment Spatially-Derived Radiomics From Optical Coherence Tomography Predict Anti-VEGF Treatment Durability in Macular Edema Secondary to Retinal Vascular Disease: Preliminary Findings.
Multi-Compartment Spatially-Derived Radiomics From Optical Coherence Tomography Predict Anti-VEGF Treatment Durability in Macular Edema Secondary to Retinal Vascular Disease: Preliminary Findings.
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DOI：
10.1109/jtehm.2021.3096378
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发表时间：
2021
影响因子：
3.4
通讯作者：
Ehlers JP
中科院分区：
文献类型：
--
作者：
Sil Kar S;Sevgi DD;Dong V;Srivastava SK;Madabhushi A;Ehlers JP
目的：糖尿病性黄斑水肿（DME）和视网膜静脉阻塞（RVO）是世界范围内视力损害的主要原因。血管内皮生长因子（VEGF）刺激血视网膜屏障的破坏，导致黄斑内液体积聚。抗VEGF治疗是这两种疾病的一线治疗;然而，个体患者的反应程度不同。这项工作的主要目的是识别（i）基线谱域光学相干断层扫描（SD-OCT）图像的单个液体和视网膜组织区域内基于纹理的放射组学特征，以及（ii）有助于预测治疗反应的最相关特征的特定空间区域。研究方法：从从PERMEATE研究获得的OCT图像的每个液体和视网膜组织室中提取总共962个基于纹理的放射组学特征。结合四种不同的机器学习分类器对从不同特征选择方法的共识中选择的最佳性能特征进行了评估：线性判别分析（LDA）、二次判别分析（QDA）、随机森林（RF）和支持向量机（SVM）在交叉验证方法中用于区分耐受延长间隔给药（非反弹者）和需要更频繁给药（反弹者）的眼睛。结果如下：液体和视网膜组织特征的组合产生了0.78±0.08的交叉验证的受试者工作特征曲线下面积（AUC），用于区分反弹者和非反弹者。结论：这项研究表明，与IRF亚室相关的基于纹理的放射组学特征在抗VEGF治疗反弹者和非反弹者之间最具鉴别力。临床影响：通过进一步验证，基于OCT的成像生物标志物可用于DME患者的治疗管理。
Objective: Diabetic macular edema (DME) and retinal vein occlusion (RVO) are the leading causes of visual impairments across the world. Vascular endothelial growth factor (VEGF) stimulates breakdown of blood-retinal barrier that causes accumulation of fluid within macula. Anti-VEGF therapy is the first-line treatment for both the diseases; however, the degree of response varies for individual patients. The main objective of this work was to identify the (i) texture-based radiomics features within individual fluid and retinal tissue compartments of baseline spectral-domain optical coherence tomography (SD-OCT) images and (ii) the specific spatial compartments that contribute most pertinent features for predicting therapeutic response. Methods: A total of 962 texture-based radiomics features were extracted from each of the fluid and retinal tissue compartments of OCT images, obtained from the PERMEATE study. Top-performing features selected from the consensus of different feature selection methods were evaluated in conjunction with four different machine learning classifiers: Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Random Forest (RF), and Support Vector Machine (SVM) in a cross-validated approach to distinguish eyes tolerating extended interval dosing (non-rebounders) and those requiring more frequent dosing (rebounders). Results: Combination of fluid and retinal tissue features yielded a cross-validated area under receiver operating characteristic curve (AUC) of 0.78±0.08 in distinguishing rebounders from non-rebounders. Conclusions: This study revealed that the texture-based radiomics features pertaining to IRF subcompartment were most discriminating between rebounders and non-rebounders to anti-VEGF therapy. Clinical Impact: With further validation, OCT-based imaging biomarkers could be used for treatment management of DME patients.