Department of Medicine - DIMED, University of Padova, Padova, Italy.
Hematology and Oncology Division, Department of Women's and Children's Health, University of Padova, Padova, Italy.
Radiol Oncol. 2022 Mar 28;56(2):138-141. doi: 10.2478/raon-2022-0013.
Pediatric soft tissue sarcomas are rare tumors with rhabdomyosarcoma being the most frequent histotype. Diagnostic imaging plays a significant role in the evaluation of this type of tumors. Thus, aim of this study was to assess the prognostic and diagnostic value of radiomic features extracted from axial T2w images of the primary lesion in children with soft tissue sarcomas examined by PET/MR for staging.
Using an open source software, each lesion was segmented and 33 radiomic features then extracted. Factor and logistic regression analyses were applied to select highly correlating features and evaluate their prognostic role, respectively. Differences in radiomic, demographics, metabolic, and laboratory variables according to tumor grade and histotype were investigated by the Students' and Chi-square tests. In case of differences the diagnostic value of the variable/s was assessed by receiver operating curves.
Eighteen children (11 female; mean age 7.8 ± 4.6-year-old) matched the inclusion criteria. The factor analysis allowed the selection of five highly correlating features which, according to regression analysis, did not influence the outcome (p > 0.05, each). The feature lmc1 was significantly higher in low grade lesions (p = 0.045) and showed 70.4% accuracy in classifying high grade tumors while the feature variance was significantly lower in rhabdomyosarcomas (p = 0.008) and showed 83.3% accuracy for this histotype.
In conclusion, our preliminary results suggest that specific radiomic features may act as biomarkers of pediatric soft tissue sarcoma grade and histotype.
儿科软组织肉瘤是一种罕见的肿瘤，横纹肌肉瘤是最常见的组织学类型。诊断成像在评估这类肿瘤中起着重要作用。因此，本研究的目的是评估 PET/MR 分期检查的儿童软组织肉瘤原发灶轴位 T2w 图像提取的放射组学特征的预后和诊断价值。
使用开源软件对每个病灶进行分割，然后提取 33 个放射组学特征。应用因子和逻辑回归分析分别选择高度相关的特征，并评估其预后作用。采用学生 t 检验和卡方检验比较不同肿瘤分级和组织学类型的放射组学、人口统计学、代谢和实验室变量的差异。如果存在差异，则通过接受者操作曲线评估变量的诊断价值。
18 名儿童（11 名女性；平均年龄 7.8 ± 4.6 岁）符合纳入标准。因子分析允许选择五个高度相关的特征，根据回归分析，这些特征不影响结果（p > 0.05，每个特征）。低级别病变的 lmc1 特征明显较高（p = 0.045），在分类高级别肿瘤时准确率为 70.4%，而横纹肌肉瘤的方差明显较低（p = 0.008），对该组织类型的准确率为 83.3%。
总之，我们的初步结果表明，特定的放射组学特征可能是儿科软组织肉瘤分级和组织学类型的生物标志物。
