Building CT Radiomics Based Nomogram for Preoperative Esophageal Cancer Patients Lymph Node Metastasis Prediction.
Building CT Radiomics Based Nomogram for Preoperative Esophageal Cancer Patients Lymph Node Metastasis Prediction.
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建立基于CT放射组学的列线图用于食管癌患者术前淋巴结转移预测
DOI：
10.1016/j.tranon.2018.04.005
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发表时间：
2018-06
影响因子：
5
通讯作者：
Tian J
中科院分区：
文献类型：
--
作者：
Shen C;Liu Z;Wang Z;Guo J;Zhang H;Wang Y;Qin J;Li H;Fang M;Tang Z;Li Y;Qu J;Tian J
目的：建立并验证基于放射组学的食管癌术前淋巴结（LN）转移预测图。患者和方法：本研究共纳入197例食管癌患者，他们的淋巴结转移已被病理证实。数据采集时间为2016年1月至2016年5月；前三个月的患者设为训练队列，2016年4月的患者设为验证队列。从患者的计算机断层扫描（CT）图像中提取了约788个放射组学特征。利用弹性网方法对特征空间进行降维和选择。采用多变量logistic回归分析建立放射组学特征和另一个预测模态图模型。预测模态图模型由三个因素组成，具有放射组学特征，其中CT报告LN数量和位置风险水平。通过标定和决策曲线分析对所建模型的性能和有效性进行了评价。结果：选择了13个放射组学特征来构建放射组学特征。放射组学特征与淋巴结转移显著相关（P<0.001）。训练组放射组学特征表现的曲线下面积（AUC）为0.806 (95% CI: 0.732-0.881)，验证组为0.771 （95% CI: 0.632-0.910）。该模型具有良好的判别性，训练队列的Harrell’s Concordance Index为0.768 (0.672 ~ 0.864,95% CI)，验证队列的Harrell’s Concordance Index为0.754 （0.603 ~ 0.895,95% CI）。决策曲线分析表明，当阈值概率大于0.15时，我们的模型将获得收益。结论：本研究提出了一种基于放射组学的放射组学特征图，因此CT报告了疑似LN的状态和肿瘤位置的虚拟变量。该方法可应用于食管癌患者淋巴结转移的个体术前预测。
PURPOSE: To build and validate a radiomics-based nomogram for the prediction of pre-operation lymph node (LN) metastasis in esophageal cancer. PATIENTS AND METHODS: A total of 197 esophageal cancer patients were enrolled in this study, and their LN metastases have been pathologically confirmed. The data were collected from January 2016 to May 2016; patients in the first three months were set in the training cohort, and patients in April 2016 were set in the validation cohort. About 788 radiomics features were extracted from computed tomography (CT) images of the patients. The elastic-net approach was exploited for dimension reduction and selection of the feature space. The multivariable logistic regression analysis was adopted to build the radiomics signature and another predictive nomogram model. The predictive nomogram model was composed of three factors with the radiomics signature, where CT reported the LN number and position risk level. The performance and usefulness of the built model were assessed by the calibration and decision curve analysis. RESULTS: Thirteen radiomics features were selected to build the radiomics signature. The radiomics signature was significantly associated with the LN metastasis (P<0.001). The area under the curve (AUC) of the radiomics signature performance in the training cohort was 0.806 (95% CI: 0.732-0.881), and in the validation cohort it was 0.771 (95% CI: 0.632-0.910). The model showed good discrimination, with a Harrell’s Concordance Index of 0.768 (0.672 to 0.864, 95% CI) in the training cohort and 0.754 (0.603 to 0.895, 95% CI) in the validation cohort. Decision curve analysis showed our model will receive benefit when the threshold probability was larger than 0.15. CONCLUSION: The present study proposed a radiomics-based nomogram involving the radiomics signature, so the CT reported the status of the suspected LN and the dummy variable of the tumor position. It can be potentially applied in the individual preoperative prediction of the LN metastasis status in esophageal cancer patients.
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影响因子：
2
作者：
Pencina, Michael J.;D'Agostino, Ralph B., Sr.;Steyerberg, Ewout W.
通讯作者：
Steyerberg, Ewout W.
影响因子：
2.6
作者：
Ganeshan, B.;Skogen, K.;Miles, K.
通讯作者：
Miles, K.
DOI：
10.1016/j.jtho.2016.11.2226
发表时间：
2017-03
期刊：
Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer
影响因子：
--
作者：
Coroller TP;Agrawal V;Huynh E;Narayan V;Lee SW;Mak RH;Aerts HJWL
通讯作者：
Aerts HJWL
DOI：
10.1109/tsmc.1973.4309314
发表时间：
1973-01-01
期刊：
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS
影响因子：
--
作者：
HARALICK, RM;SHANMUGAM, K;DINSTEIN, I
通讯作者：
DINSTEIN, I
用于术前预测结直肠癌淋巴结转移的放射组学列线图的开发和验证
影响因子：
45.3
作者：
Huang, Yan-qi;Liang, Chang-hong;Liu, Zai-yi
通讯作者：
Liu, Zai-yi