Piao Sirong, Luo Xiao, Bao Yifang, Hu Bin, Liu Xueling, Zhu Yuqi, Yang Liqin, Geng Daoying, Li Yuxin
Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China.
Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China.
Front Neurol. 2022 Nov 3;13:998279. doi: 10.3389/fneur.2022.998279. eCollection 2022.
The differential diagnosis between autoimmune encephalitis and low-grade diffuse astrocytoma remains challenging. We aim to develop a quantitative model integrating radiomics and spatial distribution features derived from MRI for discriminating these two conditions.
In our study, we included 188 patients with confirmed autoimmune encephalitis ( = 81) and WHO grade II diffuse astrocytoma ( = 107). Patients with autoimmune encephalitis (AE, = 59) and WHO grade II diffuse astrocytoma (AS, = 79) were divided into training and test sets, using stratified sampling according to MRI scanners. We further included an independent validation set (22 patients with AE and 28 patients with AS). Hyperintensity fluid-attenuated inversion recovery (FLAIR) lesions were segmented for each subject. Ten radiomics and eight spatial distribution features were selected the least absolute shrinkage and selection operator (LASSO), and joint models were constructed by logistic regression for disease classification. Model performance was measured in the test set using the area under the receiver operating characteristic (ROC) curve (AUC). The discrimination performance of the joint model was compared with neuroradiologists.
The joint model achieved better performance (AUC 0.957/0.908, accuracy 0.914/0.840 for test and independent validation sets, respectively) than the radiomics and spatial distribution models. The joint model achieved lower performance than a senior neuroradiologist (AUC 0.917/0.875) but higher performance than a junior neuroradiologist (AUC 0.692/0.745) in the test and independent validation sets.
The joint model of radiomics and spatial distribution from a single FLAIR could effectively classify AE and AS, providing clinical decision support for the differential diagnosis between the two conditions.
自身免疫性脑炎与低级别弥漫性星形细胞瘤的鉴别诊断仍然具有挑战性。我们旨在开发一种整合来自MRI的放射组学和空间分布特征的定量模型，以区分这两种疾病。
在我们的研究中，纳入了188例确诊为自身免疫性脑炎（n = 81）和世界卫生组织II级弥漫性星形细胞瘤（n = 107）的患者。根据MRI扫描仪，采用分层抽样将自身免疫性脑炎（AE，n = 59）和世界卫生组织II级弥漫性星形细胞瘤（AS，n = 79）患者分为训练集和测试集。我们还纳入了一个独立验证集（22例AE患者和28例AS患者）。对每个受试者的高信号液体衰减反转恢复（FLAIR）病变进行分割。通过最小绝对收缩和选择算子（LASSO）选择了10个放射组学特征和8个空间分布特征，并通过逻辑回归构建联合模型进行疾病分类。在测试集中使用受试者操作特征（ROC）曲线下面积（AUC）来衡量模型性能。将联合模型的鉴别性能与神经放射科医生进行比较。
联合模型的性能（测试集和独立验证集的AUC分别为0.957/0.908，准确率分别为0.914/0.840）优于放射组学模型和空间分布模型。在测试集和独立验证集中，联合模型的性能低于资深神经放射科医生（AUC为0.917/0.875），但高于初级神经放射科医生（AUC为0.692/0.745）。
基于单个FLAIR的放射组学和空间分布联合模型可以有效地对AE和AS进行分类，为这两种疾病的鉴别诊断提供临床决策支持。
