Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, 400014, China (S.D., H.Z., L.W., X.F., X.Y., Z.H., X.Z., J.C.).
Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, 400000, China (Z.Y.).
Acad Radiol. 2024 May;31(5):2085-2096. doi: 10.1016/j.acra.2023.11.011. Epub 2023 Nov 25.
To develop MRI-based radiomics models from the lesion level to the subject level and assess their value for differentiating myelin oligodendrocyte glycoprotein antibody-related disease (MOGAD) from non-MOGAD acute demyelinating syndromes in pediatrics.
66 MOGAD and 66 non-MOGAD children were assigned to the training set (36/35), internal test set (14/16), and external test set (16/15), respectively. At the lesion level, five single-sequence models were developed alongside a fusion model (combining these five sequences). The radiomics features of each lesion were quantified as the lesion-level radscore (LRS) using the best-performing model. Subsequently, a lesion-typing function was employed to classify lesions into two types (MOGAD-like or non-MOGAD-like), and the average LRS of the predominant type lesions in each subject was considered as the subject-level radscore (SRS). Based on SRS, a subject-level model was established and compared to both clinical models and radiologists' assessments.
At the lesion level, the fusion model outperformed the five single-sequence models in distinguishing MOGAD and non-MOGAD lesions (0.867 and 0.810 of area under the curve [AUC] in internal and external testing, respectively). At the subject level, the SRS model showed superior performance (0.844 and 0.846 of AUC in internal and external testing, respectively) compared to clinical models and radiologists' assessments for distinguishing MOGAD and non-MOGAD.
MRI-based radiomics models have potential clinical value for identifying MOGAD from non-MOGAD. The fusion model and SRS model can distinguish between MOGAD and non-MOGAD at the lesion level and subject level, respectively, providing a differential diagnosis method for these two diseases.
从病灶水平到个体水平建立基于 MRI 的放射组学模型,并评估其在鉴别儿科髓鞘少突胶质细胞糖蛋白抗体相关性疾病(MOGAD)与非 MOGAD 急性脱髓鞘综合征中的价值。
66 例 MOGAD 和 66 例非 MOGAD 患儿分别被纳入训练集(36/35)、内部测试集(14/16)和外部测试集(16/15)。在病灶水平,建立了 5 个单序列模型,并结合这些序列建立了融合模型。使用表现最佳的模型对每个病灶的放射组学特征进行量化,得到病灶水平 radscore(LRS)。随后,采用病灶分型功能将病灶分为两种类型(MOGAD 样或非 MOGAD 样),并将每个个体中优势类型病灶的平均 LRS 作为个体水平 radscore(SRS)。基于 SRS,建立个体水平模型,并与临床模型和放射科医生评估进行比较。
在病灶水平,融合模型在区分 MOGAD 和非 MOGAD 病灶方面优于 5 个单序列模型(内部和外部测试的曲线下面积 AUC 分别为 0.867 和 0.810)。在个体水平,SRS 模型的表现优于临床模型和放射科医生评估(内部和外部测试的 AUC 分别为 0.844 和 0.846),在鉴别 MOGAD 和非 MOGAD 方面具有潜在的临床价值。
基于 MRI 的放射组学模型在鉴别 MOGAD 与非 MOGAD 方面具有潜在的临床价值。融合模型和 SRS 模型可分别在病灶水平和个体水平区分 MOGAD 和非 MOGAD,为这两种疾病提供了一种鉴别诊断方法。