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首页> 外文OA文献 >The Performance of a Dual-Energy CT Derived Radiomics Model in Differentiating Serosal Invasion for Advanced Gastric Cancer Patients After Neoadjuvant Chemotherapy: Iodine Map Combined With 120-kV Equivalent Mixed Images
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The Performance of a Dual-Energy CT Derived Radiomics Model in Differentiating Serosal Invasion for Advanced Gastric Cancer Patients After Neoadjuvant Chemotherapy: Iodine Map Combined With 120-kV Equivalent Mixed Images

机译：双能CT衍生的辐射瘤模型在鉴别腹腔癌患者中的血清症侵袭后Neoadjuvant化疗后：碘映射与120 kV等效混合图像相结合

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ObjectivesThe aim was to determine whether the dual-energy CT radiomics model derived from an iodine map (IM) has incremental diagnostic value for the model based on 120-kV equivalent mixed images (120 kVp) in preoperative restaging of serosal invasion with locally advanced gastric cancer (LAGC) after neoadjuvant chemotherapy (NAC).MethodsA total of 155 patients (110 in the training cohort and 45 in the testing cohort) with LAGC who had standard NAC before surgery were retrospectively enrolled. All CT images were analyzed by two radiologists for manual classification. Volumes of interests (VOIs) were delineated semi-automatically, and 1,226 radiomics features were extracted from every segmented lesion in both IM and 120 kVp images, respectively. Spearman’s correlation analysis and the least absolute shrinkage and selection operator (LASSO) penalized logistic regression were implemented for filtering unstable and redundant features and screening out vital features. Two predictive models (120 kVp and IM-120 kVp) based on 120 kVp selected features only and 120 kVp combined with IM selected features were established by multivariate logistic regression analysis. We then build a combination model (ComModel) developed with IM-120 kVp signature and ycT. The performance of these three models and manual classification were evaluated and compared.ResultThree radiomics models showed great predictive accuracy and performance in both the training and testing cohorts (ComModel: AUC: training, 0.953, testing, 0.914; IM-120 kVp: AUC: training, 0.953, testing, 0.879; 120 kVp: AUC: training, 0.940, testing, 0.831). All these models showed higher diagnostic accuracy (ComModel: 88.9%, IM-120 kVp: 84.4%, 120 kVp: 80.0%) than manual classification (68.9%) in the testing group. ComModel and IM-120 kVp model had better performances than manual classification both in the training (both p<0.001) and testing cohorts (p<0.001 and p=0.034, respectively).ConclusionsDual-energy CT-based radiomics models demonstrated convincible diagnostic performance in differentiating serosal invasion in preoperative restaging for LAGC. The radiomics features derived from IM showed great potential for improving the diagnostic capability.
机译：ObjectivesThe目的是确定所述双能CT radiomics模型从碘地图（IM）衍生是否具有用于基于在浆膜侵犯的术前重新分期120千伏等效混合图像（120的kVp）的模型局部晚期胃癌增量诊断价值新辅助化疗（NAC）.MethodsA共有155 LAGC例（在训练组110和45的测试组）谁了标准NAC手术进行回顾性入选后前癌（LAGC）。所有的CT图像由两名医生为手工分类分析。的利益体积（VOI的）被半自动地描绘，并从分别在两个IM每分割病变和120个的kVp图像，提取1226个radiomics特征。 Spearman相关分析和最小绝对收缩和选择算子（LASSO）罚逻辑回归进行用于过滤和不稳定冗余特征和筛选出来的重要特征实现。仅基于120个kVp的选择的特征和120的kVp与IM结合了两种预测模型（120的kVp和IM-120的kVp）选择的特征，通过多变量logistic回归分析建立。然后，我们建立与IM-120 kVp的签名和YCT开发的相结合的模式（ComModel）。这三种模式和手动分类的性能进行了评估，并compared.ResultThree radiomics车型表现出极大的预测准确性和两个训练表现和测试同伙（ComModel：AUC：培训，0.953，测试，0.914; IM-120的kVp：AUC：训练，0.953，测试，0.879; 120 kVp的：AUC：训练，0.940，测试，0.831）。所有这些模型显示出更高的诊断准确度（ComModel：88.9％，IM-120的kVp：84.4％，120的kVp：80.0％）比试验组中的人工分类（68.9％）。 ComModel和IM-120的kVp模型具有比手工分类更好的性能都在训练（均为P <0.001）和测试组群（P <0.001和p = 0.034，分别地）.ConclusionsDual能CT基于radiomics模型中证明说服力诊断性能在对LAGC术前再分期区分浆膜侵犯。从IM获得的radiomics特点表现为提高诊断能力的巨大潜力。

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