The Institute of Image Processing and Pattern Recognition, Department of Automation, Shanghai Jiao Tong University (SJTU), 800 Dongchuan RD. Minhang District, Shanghai, 200240, People's Republic of China.
Department of Preventative Ophthalmology, Shanghai Eye Diseases Prevention and Treatment Center, Shanghai Eye Hospital, No. 380 Kangding Road, Shanghai, 200040, China.
J Transl Med. 2021 Apr 26;19(1):167. doi: 10.1186/s12967-021-02818-1.
Myopic maculopathy (MM) is the most serious and irreversible complication of pathologic myopia, which is a major cause of visual impairment and blindness. Clinic proposed limited number of factors related to MM. To explore additional features strongly related with MM from optic disc region, we employ a machine learning based radiomics analysis method, which could explore and quantify more hidden or imperceptible MM-related features to the naked eyes and contribute to a more comprehensive understanding of MM and therefore may assist to distinguish the high-risk population in an early stage.
A total of 457 eyes (313 patients) were enrolled and were divided into severe MM group and without severe MM group. Radiomics analysis was applied to depict features significantly correlated with severe MM from optic disc region. Receiver Operating Characteristic were used to evaluate these features' performance of classifying severe MM.
Eight new MM-related image features were discovered from the optic disc region, which described the shapes, textural patterns and intensity distributions of optic disc region. Compared with clinically reported MM-related features, these newly discovered features exhibited better abilities on severe MM classification. And the mean values of most features were markedly changed between patients with peripapillary diffuse chorioretinal atrophy (PDCA) and macular diffuse chorioretinal atrophy (MDCA).
Machine learning and radiomics method are useful tools for mining more MM-related features from the optic disc region, by which complex or even hidden MM-related features can be discovered and decoded. In this paper, eight new MM-related image features were found, which would be useful for further quantitative study of MM-progression. As a nontrivial byproduct, marked changes between PDCA and MDCA was discovered by both new image features and clinic features.
病理性近视是一种严重的进行性致盲性疾病，其最严重和不可逆转的并发症是近视性黄斑病变（MM）。临床提出了与 MM 相关的少数因素。为了从视盘区域探索与 MM 强相关的其他特征，我们采用了基于机器学习的放射组学分析方法，该方法可以探索和量化更多肉眼看不见或难以察觉的 MM 相关特征，从而更全面地了解 MM，并可能有助于在早期区分高危人群。
共纳入 457 只眼（313 例患者），分为重度 MM 组和无重度 MM 组。从视盘区域提取与重度 MM 显著相关的放射组学特征。使用受试者工作特征曲线来评估这些特征对重度 MM 分类的性能。
从视盘区域发现了 8 个新的 MM 相关图像特征，这些特征描述了视盘区域的形状、纹理模式和强度分布。与临床报道的 MM 相关特征相比，这些新发现的特征在重度 MM 分类方面表现出更好的能力。并且，在伴有脉络膜视网膜弥漫性萎缩的周边型（PDCA）和黄斑弥漫性萎缩的黄斑型（MDCA）患者之间，大多数特征的平均值有明显差异。
机器学习和放射组学方法是从视盘区域挖掘更多 MM 相关特征的有用工具，通过这些方法可以发现和解析复杂甚至隐藏的 MM 相关特征。本文发现了 8 个新的 MM 相关图像特征，这将有助于对 MM 进展进行进一步的定量研究。作为一个非平凡的副产品，通过新的图像特征和临床特征都发现了 PDCA 和 MDCA 之间的显著差异。
