Misdiagnosis of multiple sclerosis (MS) and neuromyelitis optica (NMO) may delay the treatment, resulting in poor prognosis. However, the precise identification of these two diseases is still challenging in clinical practice. We aimed to evaluate the value of quantitative radiomic features extracted from the brain white matter lesions for differential diagnosis of MS and NMO. We recruited 116 CNS demyelinating patients including 78 MS, and 38 NMO. Three neuroradiologists performed visual differential diagnosis based on brain MRI for comparison purpose. A multi-level scheme was designed to harness the selection of discriminative and stable radiomics features extracted from brain while mater lesions in T1-MPRAGE, T2 sequences and clinical factors. Based on the imaging phenotype composed of the selected radiomic and clinical features, Multi-parametric Multivariate Random Forest (MM-RF) model was constructed and verified with both 10-fold cross-validation and independent testing. Result interpretation was provided to build trust in diagnostic decisions. Eighty-six patients were randomly selected to form the training set while the rest 30 patients for independent testing. On the training set, our MM-RF model achieved accuracy 0.849 and AUC 0.826 in 10-fold cross-validation, which were significantly higher than clinical visual analysis (0.709 and 0.683, p < 0.05). In the independent testing, the MM-RF model achieved AUC 0.902, accuracy 0.871, sensitivity 0.873, specificity 0.869, respectively. Furthermore, age, sex and EDSS were found mildly correlated with the radiomic features (p of all < 0.05). Multi-parametric radiomic features have potential as practical quantitative imaging biomarkers for differentiating MS from NMO.

中文翻译：

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具有可信机器学习的多参数 MRI 表型用于区分中枢神经系统脱髓鞘疾病

多发性硬化症（MS）和视神经脊髓炎（NMO）的误诊可能会延误治疗，导致预后不良。然而，这两种疾病的精确识别在临床实践中仍然具有挑战性。我们旨在评估从脑白质病变中提取的定量放射组学特征对 MS 和 NMO 鉴别诊断的价值。我们招募了 116 名 CNS 脱髓鞘患者，包括 78 名 MS 和 38 名 NMO。三位神经放射科医生基于脑部 MRI 进行视觉鉴别诊断以进行比较。设计了一个多级方案，以利用从 T1-MPRAGE、T2 序列和临床因素中的大脑和母体病变中提取的有区别和稳定的放射组学特征的选择。基于由选定的影像学和临床特征组成的成像表型，多参数多元随机森林 (MM-RF) 模型被构建并通过 10 倍交叉验证和独立测试进行验证。提供结果解释以建立对诊断决策的信任。随机选择 86 名患者形成训练集，其余 30 名患者进行独立测试。在训练集上，我们的 MM-RF 模型在 10 倍交叉验证中实现了 0.849 和 AUC 0.826 的准确度，显着高于临床视觉分析（0.709 和 0.683，p < 0.05）。在独立测试中，MM-RF模型分别达到了AUC 0.902、准确度0.871、灵敏度0.873、特异性0.869。此外，发现年龄、性别和 EDSS 与放射组学特征轻度相关（p 均 < 0.05）。