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.
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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双能 CT 衍生的放射组学模型在区分晚期胃癌患者新辅助化疗后浆膜侵袭方面的表现：碘图与 120 kV 等效混合图像相结合
DOI：
10.3389/fonc.2020.562945
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发表时间：
2020
影响因子：
4.7
通讯作者：
Zhang H
中科院分区：
文献类型：
--
作者：
Wang L;Zhang Y;Chen Y;Tan J;Wang L;Zhang J;Yang C;Ma Q;Ge Y;Xu Z;Pan Z;Du L;Yan F;Yao W;Zhang H
目的：确定基于120 kV等效混合图像（120 kVp）的碘图（IM）的双能量CT放射组学模型在新辅助化疗（NAC）后局部进展期胃癌（LAGC）浸润的术前再分期中是否具有增量诊断价值。方法回顾性研究155例LAGC患者，其中培训组110例，试验组45例。所有CT图像由两名放射科医师进行手动分类分析。半自动地描绘感兴趣区域（VOI），并且分别从IM和120 kVp图像中的每个分割的病变中提取1，226个放射组学特征。采用斯皮尔曼相关分析和最小绝对收缩和选择算子（LASSO）惩罚逻辑回归方法过滤不稳定和冗余特征，筛选出重要特征。通过多因素Logistic回归分析建立了仅基于120 kVp选择特征和120 kVp结合IM选择特征的两种预测模型（120 kVp和IM-120 kVp）。然后，我们建立了一个组合模型（ComModel）开发的IM-120 kVp签名和ycT。对这三种模型与人工分类的性能进行了评价和比较。结果三个放射组学模型在训练和测试队列中均显示出较高的预测准确性和性能（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）。在试验组中，所有这些模型的诊断准确率（ComModel：88.9%，IM-120 kVp：84.4%，120 kVp：80.0%）均高于人工分类（68.9%）。ComModel和IM-120 kVp模型在训练组（均p<0.001）和测试组（分别p <0.001和p=0.034）中的性能均优于手动分类。结论基于双能CT的放射组学模型在LAGC术前再分期中鉴别浆膜浸润方面表现出了令人信服的诊断性能。来自IM的放射组学特征显示出提高诊断能力的巨大潜力。
Objectives The 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). Methods A 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. Result Three 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). Conclusions Dual-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.
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