探讨双层能谱探测器计算机断层扫描 (DLSCT) 衍生的碘图的放射组学分析在结直肠癌 (CRC) 患者术前预测肿瘤沉积 (TD) 中的价值。共有 264 名经病理证实的 CRC 患者 (TD)回顾性纳入来自两家医院术前接受 DLSCT 的 TDs（n = 80）；TDs -（n = 184），分为训练组（n = 124）、测试组（n = 54）和外部验证队列（n = 86） ）。分析并测量常规CT特征和碘浓度（IC）。放射组学特征源自 DLSCT 的静脉相碘图。使用最小绝对收缩和选择算子（LASSO）进行特征选择。最后，采用支持向量机（SVM）算法来开发基于最有价值的临床参数和放射组学特征的临床、放射组学和组合模型。使用受试者工作特征曲线下面积 (AUC)、校准曲线和决策曲线分析来评估模型的功效。结合了有价值的临床参数和放射组学特征的组合模型在预测 CRC 中表现出优异的性能（AUC 分别为 0.926、0.881）和 0.887（在训练、测试和外部验证队列中分别为 0.887），优于训练队列和外部验证队列中的临床模型（AUC：0.839 和 0.695；p：0.003 和 0.014）以及两个队列中的放射组学模型（AUC：0.922和0.792；p：0.014和0.035）。DLSCT碘图谱的放射组学分析对术前诊断CRC中的TD具有良好的预测效率，可以指导临床医生制定个体化治疗策略。基于DLSCT碘图的放射组学模型地图有可能有助于术前准确预测 CRC 患者的 TD，为临床决策提供有价值的指导。根据传统 CT 特征在术前准确预测 CRC 患者的 TD 提出了挑战。基于 DLSCT 碘图的放射组学模型在预测 TD 方面优于传统 CT。该模型结合了 DLSCT 碘图放射组学特征和传统 CT 特征，在预测 TD 方面表现出色。© 2024。作者获得欧洲放射学会的独家许可。

To investigate the value of radiomics analysis of dual-layer spectral-detector computed tomography (DLSCT)-derived iodine maps for predicting tumor deposits (TDs) preoperatively in patients with colorectal cancer (CRC).A total of 264 pathologically confirmed CRC patients (TDs + (n = 80); TDs - (n = 184)) who underwent preoperative DLSCT from two hospitals were retrospectively enrolled, and divided into training (n = 124), testing (n = 54), and external validation cohort (n = 86). Conventional CT features and iodine concentration (IC) were analyzed and measured. Radiomics features were derived from venous phase iodine maps from DLSCT. The least absolute shrinkage and selection operator (LASSO) was performed for feature selection. Finally, a support vector machine (SVM) algorithm was employed to develop clinical, radiomics, and combined models based on the most valuable clinical parameters and radiomics features. Area under receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis were used to evaluate the model's efficacy.The combined model incorporating the valuable clinical parameters and radiomics features demonstrated excellent performance in predicting TDs in CRC (AUCs of 0.926, 0.881, and 0.887 in the training, testing, and external validation cohorts, respectively), which outperformed the clinical model in the training cohort and external validation cohorts (AUC: 0.839 and 0.695; p: 0.003 and 0.014) and the radiomics model in two cohorts (AUC: 0.922 and 0.792; p: 0.014 and 0.035).Radiomics analysis of DLSCT-derived iodine maps showed excellent predictive efficiency for preoperatively diagnosing TDs in CRC, and could guide clinicians in making individualized treatment strategies.The radiomics model based on DLSCT iodine maps has the potential to aid in the accurate preoperative prediction of TDs in CRC patients, offering valuable guidance for clinical decision-making.Accurately predicting TDs in CRC patients preoperatively based on conventional CT features poses a challenge. The Radiomics model based on DLSCT iodine maps outperformed conventional CT in predicting TDs. The model combing DLSCT iodine maps radiomics features and conventional CT features performed excellently in predicting TDs.© 2024. The Author(s), under exclusive licence to European Society of Radiology.