中国临床医学影像杂志2026，Vol.37Issue(8)：561-566,6.DOI:10.12117/jccmi.2026.08.007
光谱CT碘密度图联合影像组学预测食管癌新辅助化疗联合免疫治疗病理完全缓解的价值
Radiomics analysis of DLSCT-derived iodine density maps for predicting the pathological complete response of esophageal cancer after nCIT
摘要
Abstract
Objective:To investigate the value of radiomics analysis of dual-layer spectral detector CT(DLSCT)-derived iodine maps for evaluating the efficacy of neoadjuvant chemo-immunotherapy(nCIT)for esophageal cancer.Methods:This ret-rospective study included 129 patients with pathologically confirmed esophageal squamous cell carcinoma(ESCC)who under-went nCIT followed by surgery at the First Affiliated Hospital of Zhengzhou University between October 2022 and January 2025.Patients were categorized into pCR and non-pCR groups based on tumor regression grade(TRG).They were randomly divided into a training set(n=90)and a validation set(n=39)in a 7∶3 ratio.All patients underwent enhanced spectral CT scan before nCIT,and spectral parameters including iodine density,normalized iodine density,effective atomic number,and spectral curve slope were measured and calculated.Regions of interest(ROI)were delineated on venous-phase iodine density maps for radiomic feature extraction.The feature correlation analysis and F-test were used to select the optimal features.Univariate and multivariate Logistic regression analyses were used to identify independent predictors of pCR.Clinical,spec-tral,radiomics,and combined models were constructed based on linear discriminant analysis.Model performance was evalu-ated using receiver operating characteristic(ROC)curves and classification accuracy.Five-fold cross-validation was applied to the training set,and the models were further assessed in the validation set.Results:Multivariate Logistic regression analy-sis showed that the degree of differentiation,clinical N stage,and normalized iodine density in the arterial and venous phases(NIDA,NIDV)as independent predictors of pCR.In the training cohort,the mean AUC values for the clinical,spec-tral,radiomics,and combined models were 0.66±0.10,0.66±0.07,0.67±0.04,and 0.85±0.09,respectively,with mean classifi-cation accuracies of 0.69±0.03,0.72±0.08,0.70±0.06,and 0.88±0.07,respectively.The combined model demonstrated the best diagnostic performance.In the validation set,the combined model achieved an AUC of 0.88 and a classification accu-racy of 0.79.Conclusions:The combined model,integrating radiomics features from DLSCT iodine density maps with clini-copathological characteristics and spectral parameters,shows favorable performance in predicting pCR after nCIT in ESCC,and has the potential to serve as a tool for individualized treatment decision-making.关键词
食管肿瘤/放化疗,辅助/体层摄影术,螺旋计算机Key words
Esophageal Neoplasms/Chemoradiotherapy,Adjuvant/Tomography,Spiral Computed分类
医药卫生引用本文复制引用
慕宛伶,罗成龙,孟新华,李峰,齐宇,党晋晋,高剑波,周悦..光谱CT碘密度图联合影像组学预测食管癌新辅助化疗联合免疫治疗病理完全缓解的价值[J].中国临床医学影像杂志,2026,37(8):561-566,6.基金项目
河南省科技攻关(252102310073) （252102310073）
郑州市基础研究与应用基础研究项目(2024ZZJCYJ007). （2024ZZJCYJ007）
