Chen Bailiang, Steinberger Olivier, Fenioux Roman, Duverger Quentin, Lambrou Tryphon, Dodin Gauthier, Blum Alain, Gondim Teixeira Pedro Augusto
IADI, Inserm 1254 Nancy, University of Lorraine, Nancy, France.
Inserm CIC-IT 1433, University of Lorraine, Nancy, France.
Res Diagn Interv Imaging. 2022 Jul 2;2:100009. doi: 10.1016/j.redii.2022.100009. eCollection 2022 Jun.
To determine which combination of imaging modalities/contrast, radiomics models, and how many features provides the best diagnostic performance for the differentiation between low- and high-grade soft tissue sarcomas (STS) using a radiomics approach.
MRI and CT from 39 patients with a histologically confirmed STS were prospectively analyzed. Images were evaluated both quantitatively by radiomics models and qualitatively by visual evaluation (used as reference) for grading (low-grade vs high-grade). In radiomics analysis, 120 radiomic features were extracted and contributed into three models: least absolute shrinkage and selection operator with logistic regression(LASSO-LR), recursive feature elimination and cross-validation (RFECV-SVC) and analysis of variance with SVC (ANOVA-SVC). Those were applied to different combinations of imaging modalities acquisition, with and without contrast medium administration, as well as selected number of features.
Fat-saturated T2w (FS-T2w) MR images using RFECV-SVC radiomic models involving five features yielded the best results with mean sensitivity, specificity, and accuracy of 92% ± 10%, 78% ± 30%, and 89% ± 12%, respectively. The performance of radiomics was better than that of conventional analysis (67% accuracy) for STS grading. Combination of multiple contrast or imaging modalities did not increase the diagnostic performance.
FS-T2w MR images alone with a five-feature radiomics analysis usingh REFCV-SVC model may be able to provide sufficient diagnositic performance compared to conventional visual evaluation with multiple MRI contrast and CT imaging.
采用放射组学方法确定哪种成像方式/对比剂组合、放射组学模型以及多少特征能为区分低级别和高级别软组织肉瘤（STS）提供最佳诊断性能。
对39例经组织学证实为STS的患者的MRI和CT进行前瞻性分析。图像通过放射组学模型进行定量评估，并通过视觉评估（作为参考）进行定性评估以进行分级（低级别与高级别）。在放射组学分析中，提取120个放射组学特征并纳入三个模型：带逻辑回归的最小绝对收缩和选择算子（LASSO-LR）、递归特征消除和交叉验证（RFECV-SVC）以及带SVC的方差分析（ANOVA-SVC）。将这些模型应用于不同的成像方式采集组合，包括使用和不使用对比剂，以及选定数量的特征。
使用包含五个特征的RFECV-SVC放射组学模型的脂肪饱和T2加权（FS-T2w）MR图像产生了最佳结果，平均灵敏度、特异性和准确率分别为92%±10%、78%±30%和89%±12%。对于STS分级，放射组学的性能优于传统分析（准确率67%）。多种对比剂或成像方式的组合并未提高诊断性能。
与使用多种MRI对比剂和CT成像的传统视觉评估相比，单独使用FS-T2w MR图像并结合使用REFCV-SVC模型进行五特征放射组学分析可能能够提供足够的诊断性能。
