Department of Radiology, Beijing Tongren Hospital, Capital Medical University, No. 1 of Dongjiaominxiang, Dongcheng District, Beijing, 100730, China.
CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China.
Eur Radiol. 2018 Sep;28(9):3872-3881. doi: 10.1007/s00330-018-5381-7. Epub 2018 Apr 9.
To assess the value of the MR-based radiomics signature in differentiating ocular adnexal lymphoma (OAL) and idiopathic orbital inflammation (IOI).
One hundred fifty-seven patients with pathology-proven OAL (84 patients) and IOI (73 patients) were divided into primary and validation cohorts. Eight hundred six radiomics features were extracted from morphological MR images. The least absolute shrinkage and selection operator (LASSO) procedure and linear combination were used to select features and build radiomics signature for discriminating OAL from IOI. Discriminating performance was assessed by the area under the receiver-operating characteristic curve (AUC). The predictive results were compared with the assessment of radiologists by chi-square test.
Five radiomics features were included in the radiomics signature, which differentiated OAL from IOI with an AUC of 0.74 and 0.73 in the primary and validation cohorts respectively. There was a significant difference between the classification results of the radiomics signature and those of a radiology resident (p < 0.05), although there was no significant difference between the results of the radiomics signature and those of a more experienced radiologist (p > 0.05).
Radiomics features have the potential to differentiate OAL from IOI.
• Clinical and imaging findings of OAL and IOI often overlap, which makes diagnosis difficult. • Radiomics features can potentially differentiate OAL from IOI non invasively. • The radiomics signature discriminates OAL from IOI at the same level as an experienced radiologist.
评估基于磁共振的放射组学特征在区分眼眶附属器淋巴瘤(OAL)和特发性眼眶炎症(IOI)中的价值。
将 157 例经病理证实的 OAL(84 例)和 IOI(73 例)患者分为原始队列和验证队列。从形态学磁共振图像中提取 806 个放射组学特征。使用最小绝对值收缩和选择算子(LASSO)程序和线性组合选择特征并构建用于区分 OAL 和 IOI 的放射组学特征。通过受试者工作特征曲线(AUC)下面积评估区分性能。通过卡方检验比较预测结果与放射科医生的评估结果。
放射组学特征中包含 5 个特征,在原始和验证队列中区分 OAL 和 IOI 的 AUC 分别为 0.74 和 0.73。放射组学特征的分类结果与放射科住院医师的评估结果存在显著差异(p < 0.05),但与更有经验的放射科医生的评估结果无显著差异(p > 0.05)。
放射组学特征具有区分 OAL 和 IOI 的潜力。
OAL 和 IOI 的临床和影像学表现常重叠,导致诊断困难。
放射组学特征可潜在地无创区分 OAL 和 IOI。
放射组学特征在区分 OAL 和 IOI 方面与有经验的放射科医生水平相当。