Quantitative dual-energy computed tomography parameters as imaging biomarkers for assessing the histopathologic grade...

Colorectal cancer (CRC) represents a major global healthcare burden. Tumor differentiation grade is a critical prognostic factor for evaluating patients with CRC. The objective of this study was to examine the value of dual-energy computed tomography (DECT) quantitative parameters as imaging biomarkers for the preoperative prediction of histopathologic CRC grade.

A total of 117 patients with pathologically confirmed CRC who underwent preoperative contrast-enhanced DECT were enrolled and divided into low-grade (n=74) and high-grade (n=43) groups. Monoenergetic 74-keV images from the arterial phase (AP), venous phase (VP), and delayed phase (DP) were reconstructed as conventional 120 kVp-like computed tomography (CT) images. Lesion CT attenuation values, iodine concentration (IC), normalized iodine concentration (NIC), effective atomic number (Zeff), and extracellular volume (ECV) were measured in three phases by two radiologists, and clinical data were collected. Intergroup comparisons and Spearman correlation analyses were conducted to determine the associations between spectral parameters and the histopathologic grade of CRC, and multivariable logistic regression analyses were applied to identify independent correlates. Three types of predictive models were established: a baseline model incorporating clinical variables and CT attenuation values, a single spectral parameter model, and combined models integrating clinical features with different spectral parameters. Receiver operating characteristic (ROC) curves and the DeLong test were used to compare the area under the curve (AUC) between the models. Bootstrap internal validation with 1,000 resamples was performed on the optimal model.

Serum carcinoembryonic antigen (CEA) concentration showed significant differences between the low-grade group and high-grade group (P=0.009), whereas triphasic 120 kVp-like CT attenuation values showed no statistical differences (P>0.05). For the AP, only IC differed significantly (P=0.011); for the VP and DP, IC, NIC, Zeff, and ECV differed significantly (P<0.05), with ECV showing the strongest correlation with histopathologic grade in CRC (r=0.650: P<0.001). Multivariable analysis identified VP IC [odds ratio (OR) =4.601; P=0.046] and ECV (OR =1.492; P<0.001) as independent correlates of high histopathologic grade in CRC, with AUCs of 0.749 and 0.889, respectively. The integrated model combining clinical indicators with VP and DP spectral parameters achieved the best performance for predicting high histopathologic grade in CRC [AUC =0.904, 95% confidence interval (CI): 0.845-0.962; bias‑corrected AUC =0.904, 95% CI: 0.837-0.954], with an accuracy, sensitivity, and specificity of 85.5%, 88.4%, and 83.8%, respectively. The DeLong test revealed that this integrated model had significantly better diagnostic performance than did the baseline model (AUC =0.538; P<0.001), whereas no statistically significant difference was observed between the integrated model and the VP + DP dual-phase model (P=0.504).

DECT quantitative parameters have potential clinical value for predicting the histopathologic grade of patients with CRC. They may serve as imaging biomarkers and provide valuable information for understanding tumor biological behavior.