光谱CT定量参数在局部进展期直肠癌术后风险分层中的价值OA

Objective To investigate the value of spectral CT parameters combined with clinicopathological characteristics for predicting postoperative disease-free survival(DFS)risk in patients with locally advanced rectal cancer(LARC).Methods This retrospective study included 198 patients with LARC who underwent preoperative spectral CT examination followed by radical surgery.Patients were randomly divided into the training cohort(n=138)and validation cohort(n=60)at a ratio of 7∶3.Venous-phase normalized iodine concentration(NICVP)and delayed-phase normalized iodine concentration(NICDP)were measured using the maximum cross-sectional region-of-interest(ROI)method and the whole-tumor ROI method.Interobserver agreement and the predictive performance of imaging models derived from the two ROI methods were compared.Cox regression analyses were subsequently performed using parameters obtained with the selected method,and clinical,imaging,and imaging-clinical combined models were developed.Model performance was evaluated using the area under the receiver operating characteristic curve(AUC)and DeLong test.The best-performing model was further used for risk stratification and nomogram construction.Results The interobserver ICCs for NICVP and NICDP measured using the whole-tumor ROI method were 0.95 and 0.82,respectively,higher than those obtained using the maximum cross-sectional ROI method(0.79 and 0.69,respectively).The corresponding imaging model also achieved a higher AUC(0.83 vs.0.75,P<0.05).Univariate Cox regression showed that histologic differentiation,T stage,N stage,lymphovascular invasion,perineural invasion,NICVP,and NICDP were significantly associated with DFS.Multivariable Cox regression analysis further identified histological low differentiation,lymphovascular invasion,perineural invasion,and NICVP as independent prognostic factors(P<0.05).The imaging-clinical combined model demonstrated superior predictive performance compared with the clinical model in both the training and validation cohorts(AUCs:0.92 vs.0.87,0.84 vs.0.75,respectively).After risk stratification based on the combined-model risk score,DFS differed significantly between the high-and low-risk groups(P<0.001).A nomogram based on this model enabled individualized prediction of 2-year DFS,with a C-index of 0.844.Conclusions The whole-tumor ROI method demonstrated higher interobserver agreement.Spectral CT quantitative parameters combined with clinicopathological characteristics can improve predictive performance in patients with LARC and may provide a reference for postoperative risk stratification.