Venous CT radiomics for predicting effect of neoadjuvant chemotherapy for locally advanced gastric cancer
10.13929/j.issn.1672-8475.2025.01.009
- VernacularTitle:静脉期CT影像组学预测新辅助化疗用于局部进展期胃癌效果
- Author:
Xiaomeng HAN
1
;
Shunli LIU
;
Jizheng LIN
;
Henan LOU
;
Hongzheng SONG
;
Bo WANG
;
Yaolin SONG
;
Xiaodan ZHAO
Author Information
1. 青岛大学附属医院放射科,山东 青岛 266003
- Publication Type:Journal Article
- Keywords:
stomach neoplasms;
tomography,X-ray computed;
neoadjuvant chemotherapy;
treatment outcome;
radiomics
- From:
Chinese Journal of Interventional Imaging and Therapy
2025;22(1):37-42
- CountryChina
- Language:Chinese
-
Abstract:
Objective To investigate the value of CT radiomics for predicting effect of neoadjuvant chemotherapy(NACT)for locally advanced gastric cancer(LAGC).Methods Totally 325 LAGC patients who received NACT were retrospectively enrolled,among them 247 were taken as training set,while the rest 78 were taken as validation set.Tumor regression scale(TRG)was evaluated according to postoperation pathology after NACT,and the efficacy of NACT was evaluated.Univariate logistic regression was used to analyze and screen clinical predictors of effect of NACT,and clinical model was constructed.Radiomics features were extracted based on venous phase enhanced CT pre-and post-NACT,and Delta radiomics features(i.e.the ratio of the difference of pre-and post-NACT radiomics features and pre-NACT radiomics features)were calculated.The best features were screened based on pre-NACT,post-NACT and Delta radiomics features to construct radiomics labels,the optimal label was screened and used to construct combined model through combining clinical model.Receiver operating characteristic(ROC)curve was plotted,and the area under the curve(AUC)was calculated to evaluate predicting efficiency of the above models.Decision curve analysis(DCA)was performed to explore the clinical value of each model.Results In training set,significant effect was found in 67 cases,but not in 180 cases,while in validation set,significant effect was found in 18 cases but not in 60 cases.Borrmann classification of LAGC before NACT was the clinical predictor(P=0.031),and clinical model was constructed,which had AUC of 0.577 and 0.520 in training and validation sets,respectively.Based on pre-NACT,post-NACT and Delta radiomics features,19,14 and 17 best features were selected,and AUC of the established radiomics labels of Pre-Rad,Post-Rad and Delta-Rad in training set was 0.672,0.796 and 0.789,while in validation set was 0.558,0.805 and 0.666,respectively.Post-Rad was the optimal label,which was used to construct combined model.AUC of the obtained combined model in training and validation sets was 0.824 and 0.818,respectively,both higher than that of clinical model(both P＜0.001)but not different with that of Post-Rad(both P＞0.05).Taken 0.4 to 0.7 as the threshold,the combined model had higher clinical net benefit than the other two.Conclusion Venous CT radiomics could effectively predict effect of NACT for LAGC.Combining with clinical features could improve its predictive efficacy.
