磁共振成像2026，Vol.17Issue(9)：25-33,48,10.DOI:10.12015/issn.1674-8034.2026.09.004
基于胸部CT体成分指标联合前列腺癌病灶与前列腺周围脂肪多参数MRI预测神经侵犯的价值
Predicting perineural invasion in prostate cancer using chest CT body composition and multiparametric MRI of prostate lesions and periprostatic fat
摘要
Abstract
Objective:To evaluate the value of preoperative chest CT-derived body composition indices combined with quantitative multiparametric magnetic resonance imaging(mpMRI)features of prostate cancer(PCa)lesions and periprostatic adipose tissue(PPAT),together with clinical laboratory indicators,for predicting perineural invasion(PNI)in PCa.Materials and Methods:Seventy five patients with pathologically confirmed PCa were retrospectively enrolled and divided into a PNI-positive group(n=35)and a PNI-negative group(n=40).Muscle and adipose tissue areas and their ratios at the T12 vertebral level were automatically quantified from non-contrast chest CT images using Total Segmentator.Quantitative parameters derived from intravoxel incoherent motion(IVIM)and mDIXON-QUANT sequences were extracted from intraprostatic lesions and PPAT on mpMRI.Regions of interest were independently delineated by two radiologists,and mean values were used after interobserver agreement was confirmed by intra-class correlation coefficient.Independent predictors of PNI were identified using univariate analysis followed by multivariate logistic regression.Single-parameter and combined prediction models were constructed and evaluated using receiver operating characteristic(ROC)analysis and area under the curve(AUC).Model performance was compared using the DeLong test,and a nomogram was developed for visualization of the combined model.Model calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test,and internal validation was performed using bootstrap resampling(1000 repetitions).Results:The PNI-positive group exhibited significantly higher muscle area[(86.54±16.42)cm2 vs.(76.52±14.84)cm2],PPAT-fat fraction(FF)[(69.37%±9.33%)vs.(62.07%±7.24%)],and IVIM-perfusion fraction(f)values[0.47(0.33,0.63)vs.0.31(0.25,0.60)]and significantly lower red blood cell(RBC)levels[(3.90±0.35)/μL vs.(4.26±0.40)/μL]compared with the PNI-negative group(all P<0.05).Multivariate analysis identified muscle area[AUC=0.668,95%confidence interval(CI):0.545 to 0.790],PPAT-FF(AUC=0.724,95%CI:0.610 to 0.839),and RBC(AUC=0.673,95%CI:0.550 to 0.796)as independent predictors of PNI.The combined model(muscle area+PPAT-FF+RBC)achieved an AUC of 0.848(95%CI:0.763 to 0.933),with a sensitivity of 71.4%and a specificity of 80.0%.Its predictive performance was significantly superior to that of each single indicator(DeLong test:combined model vs.muscle area,P=0.012;combined model vs.PPAT-FF,P=0.023;combined model vs.RBC,P=0.008).Conclusions:Integration of chest CT-based body composition metrics with mpMRI quantitative features of prostate cancer lesions and periprostatic adipose tissue,together with clinical laboratory indicators,significantly enhances the preoperative prediction of perineural invasion in prostate cancer.关键词
前列腺癌/神经侵犯/体成分分析/定量计算机断层扫描/多参数磁共振成像/预测模型Key words
prostate cancer/perineural invasion/quantitative computed tomography/body composition analysis/multiparametric magnetic resonance imaging/prediction model分类
医药卫生引用本文复制引用
王思齐,张钦和,王秀林,陈丽华,王洪凯,孙祎楠,刘爱连..基于胸部CT体成分指标联合前列腺癌病灶与前列腺周围脂肪多参数MRI预测神经侵犯的价值[J].磁共振成像,2026,17(9):25-33,48,10.基金项目
辽宁省科技计划联合计划项目(编号:2025-MSLH-199)Joint Research Program of Liaoning Provincial Science and Technology Plan(No.2025-MSLH-199). （编号:2025-MSLH-199）
