Feng Fei-Wen, Jiang Fei-Yu, Liu Yuan-Qing, Sun Qi, Hong Rong, Hu Chun-Hong, Hu Su
Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China.
Department of Neurology, The First Affiliated Hospital of Soochow University, Suzhou, China.
Eur Radiol. 2025 Jan;35(1):105-116. doi: 10.1007/s00330-024-10918-x. Epub 2024 Jul 11.
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.
探讨双层光谱探测器计算机断层扫描（DLSCT）衍生碘图的放射组学分析在预测结直肠癌（CRC）患者术前肿瘤结节（TDs）方面的价值。
回顾性纳入两家医院264例经病理证实的CRC患者（TDs阳性（n = 80）；TDs阴性（n = 184）），这些患者均接受了术前DLSCT检查，并分为训练组（n = 124）、测试组（n = 54）和外部验证队列（n = 86）。分析并测量了常规CT特征和碘浓度（IC）。放射组学特征源自DLSCT静脉期碘图。采用最小绝对收缩和选择算子（LASSO）进行特征选择。最后，基于最有价值的临床参数和放射组学特征，采用支持向量机（SVM）算法构建临床、放射组学和联合模型。采用受试者操作特征曲线下面积（AUC）、校准曲线和决策曲线分析来评估模型的效能。
结合有价值的临床参数和放射组学特征的联合模型在预测CRC患者的TDs方面表现出色（训练组、测试组和外部验证队列的AUC分别为0.926、0.881和0.887），其在训练队列和外部验证队列中的表现优于临床模型（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衍生碘图的放射组学分析在术前诊断CRC患者的TDs方面显示出优异的预测效率，并可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有潜力辅助准确术前预测CRC患者的TDs，为临床决策提供有价值的指导。
基于常规CT特征术前准确预测CRC患者的TDs具有挑战性。基于DLSCT碘图的放射组学模型在预测TDs方面优于传统CT。结合DLSCT碘图放射组学特征和常规CT特征的模型在预测TDs方面表现出色。
