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Abstract：
UNASSIGNED: 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.
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.
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.
摘要：
■要确定成像模式/对比度的组合，影像组学模型，以及使用影像组学方法，有多少特征为区分低度和高度软组织肉瘤（STS）提供了最佳诊断性能。
■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。
■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。
