Akinci D'Antonoli Tugba, Santini Francesco, Deligianni Xeni, Garcia Alzamora Meritxell, Rutz Erich, Bieri Oliver, Brunner Reinald, Weidensteiner Claudia
Department of Pediatric Radiology, University Children's Hospital Basel, Basel, Switzerland.
Department of Radiology, University Hospital of Basel, Basel, Switzerland.
Front Neurol. 2021 Mar 22;12:633808. doi: 10.3389/fneur.2021.633808. eCollection 2021.
Cerebral palsy (CP) is the most common cause of physical disability in childhood. Muscle pathologies occur due to spasticity and contractures; therefore, diagnostic imaging to detect pathologies is often required. Imaging has been used to assess torsion or estimate muscle volume, but additional methods for characterizing muscle composition have not thoroughly been investigated. MRI fat fraction (FF) measurement can quantify muscle fat and is often a part of standard imaging in neuromuscular dystrophies. To date, FF has been used to quantify muscle fat and assess function in CP. In this study, we aimed to utilize a radiomics and FF analysis along with the combination of both methods to differentiate affected muscles from healthy ones. A total of 9 patients (age range 8-15 years) with CP and 12 healthy controls (age range 9-16 years) were prospectively enrolled (2018-2020) after ethics committee approval. Multi-echo Dixon acquisition of the calf muscles was used for FF calculation. The images of the second echo (TE = 2.87 ms) were used for feature extraction from the soleus, gastrocnemius medialis, and gastrocnemius lateralis muscles. The least absolute shrinkage and selection operator (LASSO) regression was employed for feature selection. RM, FF model (FFM), and combined model (CM) were built for each calf muscle. The receiver operating characteristic (ROC) curve and their respective area under the curve (AUC) values were used to evaluate model performance. In total, the affected legs of 9 CP patients and the dominant legs of 12 healthy controls were analyzed. The performance of RM for soleus, gastrocnemius medialis, and gastrocnemius lateralis (AUC 0.92, 0.92, 0.82, respectively) was better than the FFM (AUC 0.88, 0.85, 0.69, respectively). The combination of both models always had a better performance than RM or FFM (AUC 0.95, 0.93, 0.83). FF was higher in the patient group (FF 9.1%, FF 8.5%, and FF 10.2%) than control group (FF 3.3%, FF 4.1%, FF 6.6%). The combination of MRI quantitative fat fraction analysis and texture analysis of muscles is a promising tool to evaluate muscle pathologies due to CP in a non-invasive manner.
脑性瘫痪（CP）是儿童身体残疾的最常见原因。由于痉挛和挛缩会出现肌肉病变；因此，通常需要进行诊断成像以检测病变。成像已被用于评估扭转或估计肌肉体积，但尚未对表征肌肉成分的其他方法进行全面研究。磁共振成像脂肪分数（FF）测量可对肌肉脂肪进行量化，并且通常是神经肌肉营养不良症标准成像的一部分。迄今为止，FF已被用于量化CP患者的肌肉脂肪并评估其功能。在本研究中，我们旨在利用放射组学和FF分析以及这两种方法的组合，以区分患侧肌肉和健康肌肉。在伦理委员会批准后，前瞻性招募了9例CP患者（年龄范围8 - 15岁）和12名健康对照者（年龄范围9 - 16岁）（2018 - 2020年）。采用多回波狄克逊采集小腿肌肉数据来计算FF。利用第二次回波（TE = 2.87 ms）的图像从比目鱼肌、腓肠肌内侧头和腓肠肌外侧头提取特征。采用最小绝对收缩和选择算子（LASSO）回归进行特征选择。为每条小腿肌肉构建RM、FF模型（FFM）和组合模型（CM）。采用受试者工作特征（ROC）曲线及其各自的曲线下面积（AUC）值来评估模型性能。总共分析了9例CP患者的患侧腿和12名健康对照者的优势腿。比目鱼肌、腓肠肌内侧头和腓肠肌外侧头的RM模型性能（AUC分别为0.92、0.92、0.82）优于FFM模型（AUC分别为0.88、0.85、0.69）。两种模型的组合性能总是优于RM或FFM（AUC分别为0.95、0.93、0.83）。患者组的FF较高（FF分别为9.1%、8.5%和10.2%），而对照组较低（FF分别为3.3%、4.1%、6.6%）。磁共振成像定量脂肪分数分析与肌肉纹理分析相结合，是以非侵入性方式评估CP所致肌肉病变的一种很有前景的工具。
