CT-based radiomics nomogram for differentiating gastric hepatoid adenocarcinoma from gastric adenocarcinoma: a multicentre study. (1st February 2023) - Wang, Jing; Kang, Bing; Sun, Cong; Du, Fengying; Lin, Jianxian; Ding, Fanghui; Dai, Zhengjun; Zhang, Yifei; Yang, Chenggang; Shang, Liang; Li, Leping; Hong, Qingqi; et al.
ABSTRACT: Background: To develop a CT-based radiomics nomogram for the high-precision preoperative differentiation of gastric hepatoid adenocarcinoma (GHAC) patients from gastric adenocarcinoma (GAC) patients. Research design and methods: 108 patients with GHAC from 6 centers and 108 GAC patients matched by age, sex and T stage undergoing pathological examination were retrospectively reviewed. Patients from 5 centers were divided into two cohorts (training and internal validation) at a 7:3 ratio, the remaining patients were external test cohort. Venous-phase CT images were retrieved for tumor segmentation and feature extraction. A radiomics model was developed by the least absolute shrinkage and selection operator method. The nomogram was developed by clinical factors and the radiomics score. Results: 1409 features were extracted and a radiomics model consisting of 19 features was developed, which showed a favorable performance in discriminating GHAC from GAC (AUCtraining cohort = 0.998, AUCinternal validation set = 0.942, AUCexternal test cohort = 0.731). The radiomics nomogram, including the radiomics score, AFP, and CA72_4, achieved good calibration and discrimination (AUCtraining cohort = 0.998, AUCinternal validation set = 0.954, AUCexternal test cohort = 0.909). Conclusions: The noninvasive CT-based nomogram, including radiomics score, AFP, and CA72_4, showed favorable predictive efficacy for differentiating GHAC from GAC and might be useful for clinical decision-making.