Preoperative prediction for lauren type of gastric cancer: A radiomics nomogram analysis based on CT images and clinical features

Article type: Research Article

Authors: Sun, Zongqiong^{a; *} | Jin, Linfang^b | Zhang, Shuai^c | Duan, Shaofeng^c | Xing, Wei^{d; *} | Hu, Shudong^{a; *}

Affiliations: [a] Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi City, Jiangsu, China | [b] Department of Pathology, Affiliated Hospital of Jiangnan University, Wuxi City, Jiangsu, China | [c] General Electric Company (GE) Healthcare China, Pudong New Town, Shanghai, China | [d] Department of Radiology, Third Affiliated Hospital of Soochow University, First people’s Hospital of Changzhou City, Jiangsu, China

Correspondence: [*] Corresponding authors: Zongqiong Sun, Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi City, Jiangsu, China. E-mail: qiong953780@163.com and Wei Xing, Department of Radiology, Third Affiliated Hospital of Soochow University, First people’s Hospital of Changzhou City, Jiangsu, China. E-mail: suzhxingwei@126.com and Shudong Hu, Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi City, Jiangsu, China. E-mail: anchorzjrj@aliyun.com.

Abstract: PURPOSE:To investigate feasibility of predicting Lauren type of gastric cancer based on CT radiomics nomogram before operation. MATERIALS AND METHODS:The clinical data and pre-treatment CT images of 300 gastric cancer patients with Lauren intestinal or diffuse type confirmed by postoperative pathology were retrospectively analyzed, who were randomly divided into training set and testing set with a ratio of 2:1. Clinical features were compared between the two Lauren types in the training set and testing set, respectively. Gastric tumors on CT images were manually segmented using ITK-SNAP software, and radiomic features of the segmented tumors were extracted, filtered and minimized using the least absolute shrinkage and selection operator (LASSO) regression to select optimal features and develop radiomics signature. A nomogram was constructed with radiomic features and clinical characteristics to predict Lauren type of gastric cancer. Clinical model, radiomics signature model, and the nomogram model were compared using the receiver operating characteristic (ROC) curve analysis with area under the curve (AUC). The calibration curve was used to test the agreement between prediction probability and actual clinical findings, and the decision curve was performed to assess the clinical usage of the nomogram model. RESULTS:In clinical features, Lauren type of gastric cancer relate to age and CT-N stage of patients (all p < 0.05). Radiomics signature was developed with the retained 10 radiomic features. The nomogram was constructed with the 2 clinical features and radiomics signature. Among 3 prediction models, performance of the nomogram was the best in predicting Lauren type of gastric cancer, with the respective AUC, accuracy, sensitivity and specificity of 0.864, 78.0%, 90.0%, 70.0%in the testing set. In addition, the calibration curve showed a good agreement between prediction probability and actual clinical findings (p > 0.05). CONCLUSION:The nomogram combining radiomics signature and clinical features is a useful tool with the increased value to predict Lauren type of gastric cancer.

Keywords: Gastric cancer, lauren type, radiomics, nomogram, computed tomography

DOI: 10.3233/XST-210888

Journal: Journal of X-Ray Science and Technology, vol. 29, no. 4, pp. 675-686, 2021