CT radiomics for preoperatively predicting lymphovascular invasion of gastric cancer
10.13929/j.1003-3289.201901004
- Author:
Wuchao LI
1
Author Information
1. Department of Radiology, Guizhou Provincial People's Hospital
- Publication Type:Journal Article
- Keywords:
Lymphovascular invasion;
Radiomics;
Stomach neoplasms;
Tomography, X-ray computed
- From:
Chinese Journal of Medical Imaging Technology
2019;35(7):1057-1060
- CountryChina
- Language:Chinese
-
Abstract:
Objective: To investigate the value of CT radiomics for preoperative prediction of gastric cancer lymphovascular invasion. Methods: Totally 181 patients with gastric cancer confirmed by surgical pathology were retrospectively collected and randomly divided into training set (n=120) and verification set (n=61). Firstly, the tumor area was delineated and segmented, and the radiomics features were extracted based on enhanced CT venous phase images. Then, the training set was used to screen features associated with lymphovascular invasion, and a radiomics signature was built. Finally, the model was validated based on the verification set, and ROC curve and calibration curve were used to assess the model's predictive power and fit assessment. Results: Seven radiomics features most relevant to lymphovascular invasion of gastric cancer were extracted and used to build the radiomics signature. The AUC of the training set was 0.742 (P=0.001, 95%CI [0.652, 0.831]), of the verification set was 0.727 (P=0.002, 95%CI [0.593, 0.853]). The optimal threshold based on the training set was 0.422. The accuracy, sensitivity and specificity of the model in the training set was 0.708, 0.586 and 0.806, respectively. This threshold was used for the verification set with accuracy, sensitivity, and specificity of 0.689, 0.519 and 0.824, respectively. The calibration curve showed that the radiomics signature had a good fit in both the training set and the verification set (both P>0.05). Conclusion: CT radiomics can be used as a novel non-invasive imaging method for preoperatively predicting lymphovascular invasion in gastric cancer.
