Institute of Radiology, Department of Medicine-DIMED, University of Padua, Padua, Italy.
Microsystems, Department of Mechanical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
PLoS One. 2023 May 8;18(5):e0285422. doi: 10.1371/journal.pone.0285422. eCollection 2023.
Congenital myopathies are a heterogeneous group of diseases affecting the skeletal muscles and characterized by high clinical, genetic, and histological variability. Magnetic Resonance (MR) is a valuable tool for the assessment of involved muscles (i.e., fatty replacement and oedema) and disease progression. Machine Learning is becoming increasingly applied for diagnostic purposes, but to our knowledge, Self-Organizing Maps (SOMs) have never been used for the identification of the patterns in these diseases. The aim of this study is to evaluate if SOMs may discriminate between muscles with fatty replacement (S), oedema (E) or neither (N).
MR studies of a family affected by tubular aggregates myopathy (TAM) with the histologically proven autosomal dominant mutation of the STIM1 gene, were examined: for each patient, in two MR assessments (i.e., t0 and t1, the latter after 5 years), fifty-three muscles were evaluated for muscular fatty replacement on the T1w images, and for oedema on the STIR images, for reference. Sixty radiomic features were collected from each muscle at t0 and t1 MR assessment using 3DSlicer software, in order to obtain data from images. A SOM was created to analyze all datasets using three clusters (i.e., 0, 1 and 2) and results were compared with radiological evaluation.
Six patients with TAM STIM1-mutation were included. At t0 MR assessments, all patients showed widespread fatty replacement that intensifies at t1, while oedema mainly affected the muscles of the legs and appears stable at follow-up. All muscles with oedema showed fatty replacement, too. At t0 SOM grid clustering shows almost all N muscles in Cluster 0 and most of the E muscles in Cluster 1; at t1 almost all E muscles appear in Cluster 1.
Our unsupervised learning model appears to be able to recognize muscles altered by the presence of edema and fatty replacement.
先天性肌病是一组影响骨骼肌的异质性疾病，其特点是临床表现、遗传和组织学具有高度变异性。磁共振（MR）是评估受累肌肉（即脂肪替代和水肿）和疾病进展的有价值的工具。机器学习越来越多地应用于诊断目的，但据我们所知，自组织映射（SOM）从未用于识别这些疾病的模式。本研究旨在评估 SOM 是否可以区分具有脂肪替代（S）、水肿（E）或两者都没有（N）的肌肉。
对一个受管状聚集肌病（TAM）影响的家族的 MR 研究进行了检查：对于每个患者，在两次 MR 评估（即 t0 和 t1，后者在 5 年后）中，对五十三个肌肉进行 T1w 图像上的肌肉脂肪替代和 STIR 图像上的水肿评估，以作参考。使用 3DSlicer 软件从每个肌肉的 t0 和 t1MR 评估中收集 60 个放射组学特征，以从图像中获得数据。使用三个簇（即 0、1 和 2）创建 SOM 来分析所有数据集，并将结果与放射学评估进行比较。
纳入了六名携带 TAM STIM1 突变的患者。在 t0MR 评估中，所有患者均表现出广泛的脂肪替代，在 t1 时加重，而水肿主要影响腿部肌肉，在随访时保持稳定。所有有水肿的肌肉也都有脂肪替代。在 t0 SOM 网格聚类中，几乎所有的 N 肌肉都在聚类 0 中，大多数 E 肌肉都在聚类 1 中；在 t1，几乎所有的 E 肌肉都出现在聚类 1 中。
我们的无监督学习模型似乎能够识别出受水肿和脂肪替代影响的肌肉。
