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全肝增强 CT 放射组学分析预测直肠癌术后肝转移的发生。

Whole-liver enhanced CT radiomics analysis to predict metachronous liver metastases after rectal cancer surgery.

机构信息

Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, People's Republic of China.

GE Healthcare (China), Beijing, 100176, People's Republic of China.

出版信息

Cancer Imaging. 2022 Sep 11;22(1):50. doi: 10.1186/s40644-022-00485-z.

Abstract

BACKGROUND

To develop a radiomics model based on pretreatment whole-liver portal venous phase (PVP) contrast-enhanced CT (CE-CT) images for predicting metachronous liver metastases (MLM) within 24 months after rectal cancer (RC) surgery.

METHODS

This study retrospectively analyzed 112 RC patients without preoperative liver metastases who underwent rectal surgery between January 2015 and December 2017 at our institution. Volume of interest (VOI) segmentation of the whole-liver was performed on the PVP CE-CT images. All 1316 radiomics features were extracted automatically. The maximum-relevance and minimum-redundancy and least absolute shrinkage and selection operator methods were used for features selection and radiomics signature constructing. Three models based on radiomics features (radiomics model), clinical features (clinical model), and radiomics combined with clinical features (combined model) were built by multivariable logistic regression analysis. Receiver operating characteristic (ROC) curves were used to assess the diagnostic performance of models, and calibration curve and the decision curve analysis were performed to evaluate the clinical application value.

RESULTS

In total, 52 patients in the MLM group and 60 patients in the non-MLM group were enrolled in this study. The radscore was built using 16 selected features and the corresponding coefficients. Both the radiomics model and the combined model showed higher diagnostic performance than clinical model (AUCs of training set: radiomics model 0.84 (95% CI, 0.76-0.93), clinical model 0.65 (95% CI, 0.55-0.75), combined model 0.85 (95% CI, 0.77-0.94); AUCs of validation set: radiomics model 0.84 (95% CI, 0.70-0.98), clinical model 0.58 (95% CI, 0.40-0.76), combined model 0.85 (95% CI, 0.71-0.99)). The calibration curves showed great consistency between the predicted value and actual event probability. The DCA showed that both the radiomics and combined models could add a net benefit on a large scale.

CONCLUSIONS

The radiomics model based on preoperative whole-liver PVP CE-CT could predict MLM within 24 months after RC surgery. Clinical features could not significantly improve the prediction efficiency of the radiomics model.

摘要

背景

为了开发一种基于直肠癌(RC)术后 24 个月内肝转移(MLM)的术前全肝门静脉期(PVP)对比增强 CT(CE-CT)图像的放射组学模型。

方法

本研究回顾性分析了 2015 年 1 月至 2017 年 12 月期间在我院行 RC 手术的 112 例术前无肝转移的 RC 患者。对 PVP CE-CT 图像进行全肝感兴趣区(VOI)分割。自动提取所有 1316 个放射组学特征。采用最大相关性最小冗余和最小绝对收缩和选择算子方法进行特征选择和放射组学特征构建。通过多变量逻辑回归分析建立基于放射组学特征(放射组学模型)、临床特征(临床模型)和放射组学与临床特征相结合(联合模型)的三个模型。受试者工作特征(ROC)曲线用于评估模型的诊断性能,校准曲线和决策曲线分析用于评估临床应用价值。

结果

共纳入 MLM 组 52 例,非 MLM 组 60 例。使用 16 个选定的特征和相应的系数构建 radscore。放射组学模型和联合模型的诊断性能均高于临床模型(训练集 AUC:放射组学模型 0.84(95%CI,0.76-0.93),临床模型 0.65(95%CI,0.55-0.75),联合模型 0.85(95%CI,0.77-0.94);验证集 AUC:放射组学模型 0.84(95%CI,0.70-0.98),临床模型 0.58(95%CI,0.40-0.76),联合模型 0.85(95%CI,0.71-0.99))。校准曲线显示预测值与实际事件概率之间具有很好的一致性。DCA 表明放射组学和联合模型都可以在较大范围内增加净效益。

结论

基于术前全肝 PVP CE-CT 的放射组学模型可以预测 RC 术后 24 个月内的 MLM。临床特征不能显著提高放射组学模型的预测效率。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30de/9465956/42b95f96c926/40644_2022_485_Fig1_HTML.jpg

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