MRI-based radiomics in distinguishing Kaposiform hemangioendothelioma (KHE) and fibro-adipose vascular anomaly (FAVA) in extremities: A preliminary retrospective study

医学 接收机工作特性 Lasso(编程语言) 回顾性队列研究 支持向量机 血管异常 磁共振成像 队列 放射科 无线电技术 核医学 人工智能 外科 病理 内科学 计算机科学 万维网
作者
Yingjing Ding,Zuopeng Wang,Ping Xu,Yangyang Ma,Wei Yao,Kai Li,Yandong Gong
出处
期刊:Journal of Pediatric Surgery [Elsevier]
卷期号:57 (7): 1228-1234 被引量:1
标识
DOI:10.1016/j.jpedsurg.2022.02.031
摘要

To investigate the pretreatment differentiation between Kaposiform hemangioendothelioma (KHE) and fibro-adipose vascular anomaly (FAVA) in extremities of pediatric patients. To build and validate an MRI-based radiomic model.In this retrospective study, we obtained imaging data from 43 patients. We collected and compared clinical information, sketched region of interest (ROI), and extracted radiomic features from fat-suppressed T2-weighted (T2FS) images of the two cohorts of 30 and 13 patients respectively (training versus testing cohort 7:3). To select features, we used two sample t-test and the least absolute shrinkage and selection operator (LASSO) regression. The support vector machine (SVM) classification was constructed and evaluated by receiver operating characteristic (ROC) analysis.Thirty patients with KHE and 13 patients with FAVA in the extremities were included. Most lesions demonstrated low to intermediate signal intensity on T1-weighted images and hyperintense signals on T2-weighted ones. They also showed similar traits pathologically. Initially, 107 radiomic features were acquired and then three were finally selected. The support vector machine (SVM) model was able to differentiate the two anomalies from each other with an area under the curve (AUC) of 0.807 (95%CI 0.602-1.000) and 0.846 (95%CI 0.659-1.000) in training and testing cohort, respectively.The derived radiomic features were helpful in differentiating KHE from FAVA. A model which contained these features might further improve the performance and hopefully could serve as a potential tool for identification.

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