PDF(Pubmed)
Abstract：
Geographic atrophy (GA) is an advanced form of dry age-related macular degeneration (AMD) that leads to progressive and irreversible vision loss. Identifying patients with greatest risk of GA progression is important for targeted utilization of emerging therapies. This study aimed to comprehensively evaluate the role of shape-based fractal dimension features ( F fd ) of sub-retinal pigment epithelium (sub-RPE) compartment and texture-based radiomics features ( F t ) of Ellipsoid Zone (EZ)-RPE and sub-RPE compartments for risk stratification for subfoveal GA (sfGA) progression. This was a retrospective study of 137 dry AMD subjects with a 5-year follow-up. Based on sfGA status at year 5, eyes were categorized as Progressors and Non-progressors. A total of 15 shape-based F fd of sub-RPE surface and 494 F t from each of sub-RPE and EZ-RPE compartments were extracted from baseline spectral domain-optical coherence tomography scans. The top nine features were identified from F fd and F t feature pool separately using minimum Redundancy maximum Relevance feature selection and used to train a Random Forest (RF) classifier independently using three-fold cross validation on the training set ( S t , N = 90) to distinguish between sfGA Progressors and Non-progressors. Combined F fd and F t was also evaluated in predicting risk of sfGA progression. The RF classifier yielded AUC of 0.85, 0.79 and 0.89 on independent test set ( S v , N = 47) using F fd , F t , and their combination, respectively. Using combined F fd and F t , the improvement in AUC was statistically significant on S v with p-values of 0.032 and 0.04 compared to using only F fd and only F t , respectively. Combined F fd and F t appears to identify high-risk patients. Our results show that FD and texture features could be potentially used for predicting risk of sfGA progression and future therapeutic response.
摘要：
地理萎缩(GA)是干性年龄相关性黄斑变性(AMD)的高级形式,其导致进行性和不可逆的视力丧失。确定具有最大GA进展风险的患者对于靶向利用新兴疗法很重要。本研究旨在全面评估视网膜下色素上皮（sub-RPE）隔室的基于形状的分形维数特征（Ffd）和椭球区（EZ）-RPE和亚RPE隔室的基于纹理的影像组学特征（Ft）在中央凹下GA（sfGA）进展风险分层中的作用。这是一项对137名干性AMD受试者进行5年随访的回顾性研究。根据第5年的sfGA状态，将眼睛分为进展者和非进展者。从基线光谱域光学相干断层扫描中提取了15种亚RPE表面的基于形状的Ffd和每个亚RPE和EZ-RPE区室的494Ft。使用最小冗余最大相关性特征选择分别从Ffd和Ft特征池中识别出了前9个特征，并用于在训练集上使用三折交叉验证独立训练随机森林（RF）分类器（St，N=90)以区分sfGA进展者和非进展者。还评估了Ffd和Ft联合预测sfGA进展的风险。RF分类器在独立测试集上产生0.85、0.79和0.89的AUC(Sv，N=47)使用Ffd，Ft,和他们的组合，分别。使用组合的Ffd和Ft，与仅使用Ffd和Ft相比，Sv的AUC改善具有统计学意义，p值为0.032和0.04，分别。结合Ffd和Ft似乎可以识别高危患者。我们的结果表明，FD和纹理特征可用于预测sfGA进展和未来治疗反应的风险。
