Department of Diagnostic Radiology and Radiation Oncology, Graduate School of Medicine, Chiba University, Chiba, Japan.
Department of Radiation Oncology, MR Linac ART Division, Graduate School of Medicine, Chiba University, Chiba, Japan.
Acta Radiol. 2023 Sep;64(9):2627-2635. doi: 10.1177/02841851231181680. Epub 2023 Jun 27.
Demyelinating peripheral neuropathy is characteristic of both polyneuropathy, organomegaly, endocrinopathy, M-protein, and skin changes (POEMS) syndrome and chronic inflammatory demyelinating polyneuropathy (CIDP). We hypothesized that the different pathogeneses underlying these entities would affect the sonographic imaging features.
To investigate whether ultrasound (US)-based radiomic analysis could extract features to describe the differences between CIDP and POEMS syndrome.
In this retrospective study, we evaluated nerve US images from 26 with typical CIDP and 34 patients with POEMS syndrome. Cross-sectional area (CSA) and echogenicity of the median and ulnar nerves were evaluated in each US image of the wrist, forearm, elbow, and mid-arm. Radiomic analysis was performed on these US images. All radiomic features were examined using receiver operating characteristic analysis. Optimal features were selected using a three-step feature selection method and were inputted into XGBoost to build predictive machine-learning models.
The CSAs were more enlarged in patients with CIDP than in those with POEMS syndrome without significant differences, except for that of the ulnar nerve at the wrist. Nerve echogenicity was significantly more heterogeneous in patients with CIDP than in those with POEMS syndrome. The radiomic analysis yielded four features with the highest area under the curve (AUC) value of 0.83. The machine-learning model showed an AUC of 0.90.
US-based radiomic analysis has high AUC values in differentiating POEM syndrome from CIDP. Machine-learning algorithms further improved the discriminative ability.
脱髓鞘周围神经病的特征是多发性神经病、器官肿大、内分泌病、M 蛋白和皮肤改变（POEMS）综合征和慢性炎症性脱髓鞘性多发性神经病（CIDP）。我们假设这些实体的不同发病机制会影响超声成像特征。
研究基于超声（US）的放射组学分析是否可以提取特征来描述 CIDP 和 POEMS 综合征之间的差异。
在这项回顾性研究中，我们评估了 26 例典型 CIDP 和 34 例 POEMS 综合征患者的神经 US 图像。在腕部、前臂、肘部和中臂的每个 US 图像中评估正中神经和尺神经的横截面积（CSA）和回声强度。对这些 US 图像进行放射组学分析。使用接收器操作特征分析检查所有放射组学特征。使用三步特征选择方法选择最佳特征，并将其输入 XGBoost 构建预测机器学习模型。
CIDP 患者的 CSA 比 POEMS 综合征患者更增大，但在腕部的尺神经除外。CIDP 患者的神经回声强度明显比 POEMS 综合征患者更不均匀。放射组学分析得出了四个具有最高曲线下面积（AUC）值 0.83 的特征。机器学习模型的 AUC 为 0.90。
基于 US 的放射组学分析在区分 POEMS 综合征和 CIDP 方面具有较高的 AUC 值。机器学习算法进一步提高了鉴别能力。
