Prediction of prognosis of resectable gastric cancer patients based on texture features of enhanced CT images
10.13929/j.issn.1003-3289.2020.07.025
- VernacularTitle: 基于CT增强图像纹理特征预测可切除胃癌患者预后
- Author:
Yucun HUANG
1
Author Information
1. Department of Radiology, The Fifth People's Hospital of Zhuhai
- Publication Type:Journal Article
- Keywords:
Prognosis;
Stomach neoplasms;
Texture features;
Tomography, X-ray computed
- From:
Chinese Journal of Medical Imaging Technology
2020;36(7):1046-1050
- CountryChina
- Language:Chinese
-
Abstract:
Objective: To investigate the prediction value of prognosis of resectable gastric cancer patients based on texture features of preoperative enhanced CT images. Methods:: Data of 197 patients with gastric cancer confirmed by surgical pathology were retrospectively analyzed. The patients were randomly divided into training group (n=147) and validation group (n=50). A total of 90 3-dimensional quantitative features on portal venous phase images of preoperative enhanced CT were extracted of all patients, and intraclass correlation coefficient was used to select better repetitive features. LASSO COX regression analysis was used to reduce dimensionality and screen features related to patients' overall survival (OS). A image tag was built to classify patients in 2 groups. The patients were stratified into high-risk and low-risk groups according to the median of signature score, and the difference of OS was analyzed. A nomogram integrating image tag and pathological features was constructed after analyzing the relationship of clinical, pathological features or image texture labels and prognosis of gastric cancer patients, and the efficacy in predicting prognosis of gastric cancer patients was evaluated. Clinical decision curve was plotted to evaluate relative clinical value. Results: The image tag was established with 2 OS-related CT features. Statistical differences of OS were found between high-risk and low-risk patients in both training group (χ2=9.25) and validation group (χ2=8.49, both P<0.01). The image tag and TNM staging were independent risk factors of gastric cancer. For patients in training group and validation group, AUC of image tag predicting 3-year OS was 0.72 (P=0.02) and 0.67 (P=0.07), of nomogram integrated image tag and TNM staging was 0.78 and 0.81, respectively (both P<0.01). The decision curve analysis showed that the nomogram model had higher net benefit than image tag alone with the threshold probabilities of 0.13-0.59. Conclusion: Image labels based on texture features of enhanced CT image can be used for postoperative risk stratification of gastric cancer patients. Nomogram constructed with image tag combining pathological features can help to predict the prognosis of patient with resectable gastric cancer.
