You Yaru, Liang Yan, Chen Lihong, Li Zhanzhan, Gao Beijun, Wang Xiangxiang, Yuan Mengchen, Xue Yunjing, Liu Yiyang, Gao Jianbo
Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China; Henan Key Laboratory of Imaging Diagnosis and Treatment for Digestive System Tumor, Zhengzhou 450052, China.
Department of Radiology, Sanmenxia Central Hospital, Sanmenxia 472100, China.
Eur J Radiol. 2025 May;186:112054. doi: 10.1016/j.ejrad.2025.112054. Epub 2025 Mar 16.
To investigate the value of radiomic analysis of dual-energy CT (DECT)-derived iodine maps (IMs) for the differentiation between T1/2 and T3/4a stage tumors in gastric cancer (GC).
A total of 263 patients who received upfront surgery and were pathologically confirmed with gastric adenocarcinoma were enrolled in this study. Dual-phase enhanced CT scans with gemstone spectral imaging (GSI) mode were performed within two weeks before surgery. 151 patients were retrospectively collected for the training (n = 105) and validation (n = 46) cohorts, and 112 patients were prospectively collected for the external test1 (n = 68) and external test2 (n = 44) cohorts. According to the postoperative pathological T stage, patients were classified into T1/2 and T3/4a stage groups. Clinical characteristics were recorded and quantitative iodine concentration (IC) of tumors was measured. Radiomics features were extracted from the venous phase (VP) IMs by three-dimensional region of interest (3D-ROI) segmentation. Feature selection was performed using the least absolute shrinkage and selection operator. Four machine learning algorithms, including random forest, logistic regression, naive Bayes, and support vector machine, were used to construct radiomics models. Finally, the most valuable clinical characteristics, DECT parameters, and the best radiomics model were combined to build a nomogram. The diagnostic performance of nomogram was evaluated by the area under receiver operating characteristic curve (AUC), calibration curve, and decision curve.
The nomogram combined tumor clinical T stage (cT), tumor thickness, venous-phase iodine concentration (ICVP), normalized arterial-phase iodine concentration (nICAP), and Radscore (derived from logistic regression model). This integrated model demonstrated favorable performance in the differentiation between T1/2 and T3/4a stage tumors in GC, with AUCs of 0.892 (95 %CI: 0.829-0.956), 0.846 (95 %CI: 0.734-0.958), 0.894 (95 %CI: 0.818-0.970) and 0.821 (95 %CI: 0.689-0.952) observed for the training, validation, external test 1, and external test 2 cohorts, respectively. Hosmer-Lemeshow test showed a good fit (all P > 0.05). Decision curves confirmed that the nomogram provided more net clinical benefit than the default simple strategy over a wide range of threshold probabilities.
We have developed and validated a multidimensional personalized nomogram that integrates a radiomics model based on DECT-derived IMs, DECT quantitative parameters, and traditional clinical features. The proposed model demonstrated favorable performance in preoperative identification of T3/4a stage tumors in GC.
探讨双能CT（DECT）碘图（IMs）的影像组学分析在胃癌（GC）T1/2期和T3/4a期肿瘤鉴别诊断中的价值。
本研究共纳入263例接受初次手术且经病理确诊为胃腺癌的患者。术前两周内采用宝石能谱成像（GSI）模式进行双期增强CT扫描。回顾性收集151例患者作为训练组（n = 105）和验证组（n = 46），前瞻性收集112例患者作为外部测试1组（n = 68）和外部测试2组（n = 44）。根据术后病理T分期，将患者分为T1/2期和T3/4a期组。记录临床特征并测量肿瘤的定量碘浓度（IC）。通过三维感兴趣区（3D-ROI）分割从静脉期（VP）IMs中提取影像组学特征。采用最小绝对收缩和选择算子进行特征选择。使用随机森林、逻辑回归、朴素贝叶斯和支持向量机四种机器学习算法构建影像组学模型。最后，将最有价值的临床特征、DECT参数和最佳影像组学模型相结合构建列线图。通过受试者操作特征曲线（AUC）下面积、校准曲线和决策曲线评估列线图的诊断性能。
列线图结合了肿瘤临床T分期（cT）、肿瘤厚度、静脉期碘浓度（ICVP）、动脉期归一化碘浓度（nICAP）和Radscore（源自逻辑回归模型）。该综合模型在GC的T1/2期和T3/4a期肿瘤鉴别诊断中表现出良好性能，训练组、验证组、外部测试1组和外部测试2组的AUC分别为0.892（95%CI：0.829 - 0.956）、0.846（95%CI：0.734 - 0.958）、0.894（95%CI：0.818 - 0.970）和0.821（95%CI：0.689 - 0.952）。Hosmer-Lemeshow检验显示拟合良好（所有P>0.05）。决策曲线证实，在广泛的阈值概率范围内，列线图比默认的简单策略提供了更多的净临床获益。
我们开发并验证了一种多维度个性化列线图，该列线图整合了基于DECT衍生IMs的影像组学模型、DECT定量参数和传统临床特征。所提出的模型在GC术前T3/4a期肿瘤识别中表现出良好性能。
