Dual-energy CT radiomics combined with clinical and CT features for predicting differentiation degree of gastric adenocarcinoma
10.13929/j.issn.1003-3289.2024.10.018
- VernacularTitle:双能量CT影像组学联合临床及CT特征预测胃腺癌分化程度
- Author:
Mengchen YUAN
1
;
Yiyang LIU
;
Hongliang LI
;
Lin CHEN
;
Bo DUAN
;
Shuai ZHAO
;
Yaru YOU
;
Xingzhi CHEN
;
Jianbo GAO
Author Information
1. 郑州大学第一附属医院放射科,河南郑州 450052
- Keywords:
stomach neoplasms;
radiomics;
cell differentiation;
prospective studies
- From:
Chinese Journal of Medical Imaging Technology
2024;40(10):1542-1547
- CountryChina
- Language:Chinese
-
Abstract:
Objective To observe the value of dual-energy CT(DECT)radiomics combined with clinical and CT features for predicting differentiation degree of gastric adenocarcinoma(GAC).Methods Totally 254 patients with GAC were prospectively analyzed and divided into high-grade group(low differentiation GAC,n=88)and low-grade group(middle-high differentiation GAC,n=166)according to pathological results.The patients were also divided into training set(n=203,including 70 high-grade and 133 low-grade GAC)and verification set(n=51,including 18 high-grade and 33 low-grade GAC)at the ratio of 8∶2.Radiomics features were extracted and screened based on venous phase single-level(40,70,100 and 140 keV)DECT,and a multi-energy radiomics model was constructed to predict GAC classification.Univariate analysis and multivariate logistic regression were used to analyze clinical and CT features as well as DECT parameters in training set to construct a clinic-CT model.Then a combined model was constructed through combining clinic-CT model with radiomics model.The predictive efficacy of the models were evaluated,and the calibration degree of the combined model was assessed.Results The area under the curve(AUC)of clinic-CT model,multi-energy radiomics model and combined model was 0.74,0.75 and 0.78 in training set,and 0.73,0.77 and 0.78 in verification set,respectively.Except for AUC of combined model was higher than that of clinic-CT model in training set(P＜0.05),no significant difference of AUC was found among models in training set nor verification set(all P＞0.05).The calibration degree of combined model was good in both training set and verification set(both P＞0.05).Conclusion DECT radiomics combined with clinical and CT features could effectively predict differentiation degree of GAC.
