UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
■对39例经组织学证实的STS患者的MRI和CT进行前瞻性分析。通过影像组学模型对图像进行定量评估,并通过视觉评估(用作参考)对图像进行定性评估,以进行分级(低级vs高级)。在影像组学分析中,提取了120个放射学特征,并将其贡献到三个模型中: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)以及与SVC的方差分析(ANOVA-SVC)。这些被应用于不同的成像方式采集组合,有或没有造影剂给药,以及选定的功能数量。
■使用涉及五个特征的RFECV-SVC放射组学模型的脂肪饱和T2w(FS-T2w)MR图像产生了具有平均灵敏度的最佳结果,特异性,准确率为92%±10%,78%±30%,89%±12%,分别。对于STS分级,影像组学的性能优于常规分析(67%的准确性)。多种对比或成像方式的组合并没有增加诊断性能。
■FS-T2wMR图像与使用REFCV-SVC模型的五特征影像组学分析相比于传统的多重MRI造影和CT成像视觉评估,可能能够提供足够的诊断性能。