Abstract
Abstract
Background
Preoperative prediction of the Lauren classification in gastric cancer (GC) is very important to the choice of therapy, the evaluation of prognosis, and the improvement of quality of life. However, there is not yet radiomics analysis concerning the prediction of Lauren classification straightly. In this study, a radiomic nomogram was developed to preoperatively differentiate Lauren diffuse type from intestinal type in GC.
Methods
A total of 539 GC patients were enrolled in this study and later randomly allocated to two cohorts at a 7:3 ratio for training and validation. Two sets of radiomic features were derived from tumor regions and peritumor regions on venous phase computed tomography (CT) images, respectively. With the least absolute shrinkage and selection operator logistic regression, a combined radiomic signature was constructed. Also, a tumor-based model and a peripheral ring-based model were built for comparison. Afterwards, a radiomic nomogram integrating the combined radiomic signature and clinical characteristics was developed. All the models were evaluated regarding classification ability and clinical usefulness.
Results
The combined radiomic signature achieved an area under receiver operating characteristic curve (AUC) of 0.715 (95% confidence interval [CI], 0.663–0.767) in the training cohort and 0.714 (95% CI, 0.636–0.792) in the validation cohort. The radiomic nomogram incorporating the combined radiomic signature, age, CT T stage, and CT N stage outperformed the other models with a training AUC of 0.745 (95% CI, 0.696–0.795) and a validation AUC of 0.758 (95% CI, 0.685–0.831). The significantly improved sensitivity of radiomic nomogram (0.765 and 0.793) indicated better identification of diffuse type GC patients. Further, calibration curves and decision curves demonstrated its great model fitness and clinical usefulness.
Conclusions
The radiomic nomogram involving the combined radiomic signature and clinical characteristics holds potential in differentiating Lauren diffuse type from intestinal type for reasonable clinical treatment strategy.
Funder
National Key R&D Program of China
National Natural Science Foundation of China
the Beijing Natural Science Foundation
Strategic Priority Research Program of Chinese Academy of Sciences
the Project of High-Level Talents Team Introduction in Zhuhai City
the Youth Innovation Promotion Association CAS
the Key Research Program of the Chinese Academy of Sciences
Zhenjiang Innovation Capacity Building Program (technological infrastructure) - R&D project of China
Zhenjiang first people's Hospital Fund
Jiangsu Provincial Key Research and Development Special Found
Jiangsu Innovative team leading talent fund
Jiangsu six high peak talent fund
Jiangsu 333 talent fund
Publisher
Springer Science and Business Media LLC
Subject
Radiology Nuclear Medicine and imaging,Oncology,General Medicine,Radiological and Ultrasound Technology
Cited by
34 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献