Objective To evaluate the potential of dual-energy CT (DECT) quantitative parameters and clinical characteristics in predicting pathological nodal (pN) stage in colon cancer. Materials and methods A total of 205 patients with pathologically confirmed colon cancer who underwent DECT were retrospectively enrolled and were randomly divided into training (n = 148) and test sets (n = 57) at a 7:3 ratio. The DECT quantitative parameters, including extracellular volume fraction (ECV), clinical characteristics, and Node Reporting and Data System (Node-RADS), were analyzed. Univariable and multivariable logistic regression analysis were used to construct the DECT model, Clinical_model, and Combined model. The diagnostic performance of the models was evaluated using the area under the receiver operating characteristic curve (AUC). Results Carbohydrate antigen 125 (CA125), carbohydrate antigen 242 (CA242), dual-energy index in venous phase (DEI_V), and ECV were independent factors for predicting pN+ (all p < 0.05). The Combined model (features: CA125, CA242, ECV, and DEI_V) showed significantly higher AUC than Node-RADS (0.845 vs. 0.727, p = 0.032), Clinical_model (features: CA125 and CA242) (0.845 vs. 0.692, p < 0.001), and DECT model (features: ECV and DEI_V) (0.845 vs. 0.774, p = 0.011) in the training set. The Combined model also demonstrated the highest AUC (0.849) in the test set. But there were no significant differences in AUC between the Combined model and Node-RADS (p = 0.158) in the test set. Conclusion The combination of DECT quantitative parameters and clinical characteristics showed improved diagnostic performance in predicting pN stage in colon cancer. Key Points <Emphasis Type="BoldItalic">Question</Emphasis> Does the combination of dual-energy CT (DECT) quantitative parameters and clinical characteristics predict pathological nodal (pN) stage in colon cancer? <Emphasis Type="BoldItalic">Findings</Emphasis> The Combined model showed the highest AUC for predicting pN stage in the training set, but did not show better performance than Node-RADS in the test set. <Emphasis Type="BoldItalic">Clinical relevance</Emphasis> The combination of DECT quantitative parameters and clinical characteristics may be useful for predicting pN stage in colon cancer as a noninvasive method. Graphical Abstract

Preoperative colon cancer nodal staging using dual-energy CT and clinically derived features

期刊论文
发表日期：

2026年1月16日

查看全文

Changjiu He,
Libo Lin,
Ao Yang,
Chuanyang Shao,
Jun Wang,
Yuxuan He,
Shibei Hu,
Peng Zhou,
Xiaoli Tang,
Xiaoli Chen

摘要

Objective

To evaluate the potential of dual-energy CT (DECT) quantitative parameters and clinical characteristics in predicting pathological nodal (pN) stage in colon cancer.

Materials and methods

A total of 205 patients with pathologically confirmed colon cancer who underwent DECT were retrospectively enrolled and were randomly divided into training (n = 148) and test sets (n = 57) at a 7:3 ratio. The DECT quantitative parameters, including extracellular volume fraction (ECV), clinical characteristics, and Node Reporting and Data System (Node-RADS), were analyzed. Univariable and multivariable logistic regression analysis were used to construct the DECT model, Clinical_model, and Combined model. The diagnostic performance of the models was evaluated using the area under the receiver operating characteristic curve (AUC).

Results

Carbohydrate antigen 125 (CA125), carbohydrate antigen 242 (CA242), dual-energy index in venous phase (DEI_V), and ECV were independent factors for predicting pN+ (all p < 0.05). The Combined model (features: CA125, CA242, ECV, and DEI_V) showed significantly higher AUC than Node-RADS (0.845 vs. 0.727, p = 0.032), Clinical_model (features: CA125 and CA242) (0.845 vs. 0.692, p < 0.001), and DECT model (features: ECV and DEI_V) (0.845 vs. 0.774, p = 0.011) in the training set. The Combined model also demonstrated the highest AUC (0.849) in the test set. But there were no significant differences in AUC between the Combined model and Node-RADS (p = 0.158) in the test set.

Conclusion

The combination of DECT quantitative parameters and clinical characteristics showed improved diagnostic performance in predicting pN stage in colon cancer.

Key Points

Question Does the combination of dual-energy CT (DECT) quantitative parameters and clinical characteristics predict pathological nodal (pN) stage in colon cancer?

Findings The Combined model showed the highest AUC for predicting pN stage in the training set, but did not show better performance than Node-RADS in the test set.

Clinical relevance The combination of DECT quantitative parameters and clinical characteristics may be useful for predicting pN stage in colon cancer as a noninvasive method.

Graphical Abstract