Association of CT-derived extracellular volume fraction combined with serological markers with histological grading of colorectal cancer.

This study evaluates the association between computed tomography-derived extracellular volume fraction (CT-ECV), serological markers, and histological grading in colorectal cancer, and to assess their diagnostic value in predicting poorly differentiated tumors. This retrospective study included 200 patients with pathologically confirmed primary colorectal cancer from February 2021 to May 2024. All underwent dual-phase contrast-enhanced CT and serological testing before treatment. Based on World Health Organization classification, tumors were categorized as well-differentiated (G1, n = 97), moderately differentiated (G2, n = 57), or poorly differentiated (G3, n = 46). CT-ECV and arterial phase enhancement (ΔHU) were measured. Serological markers included carcinoembryonic antigen (CEA), carbohydrate antigen 19-9 (CA19-9), carbohydrate antigen 125 (CA125), and neutrophil-to-lymphocyte ratio (NLR). Statistical methods included ANOVA, Spearman correlation, ordinal logistic regression, and receiver operating characteristic analysis. Among 200 patients (116 males, 84 females; mean age 62.7 ± 10.3 years), no significant age or sex differences were observed among differentiation groups. CT-ECV, ΔHU, and levels of CEA, CA19-9, CA125, NLR, and platelet-to-lymphocyte ratio increased with poorer differentiation (all P < .001). Multivariate analysis showed CT-ECV (per 5% increase, OR = 1.86), ΔHU (per 10 HU, OR = 1.51), CEA, CA19-9, and NLR were independent predictors of poor differentiation. CT-ECV alone yielded an area under the curve of 0.82; CT-ECV + NLR reached 0.87; and the full model (CT-ECV + CEA + CA19-9 + NLR) achieved an area under the curve of 0.91. CT-ECV correlated moderately with CA125 (ρ = 0.51) and NLR (ρ = 0.47). Sensitivity analyses confirmed model stability. CT-ECV is significantly associated with histological grade in colorectal cancer and moderately correlates with serological markers. Combining CT-ECV with NLR and tumor markers enhances noninvasive preoperative prediction of poorly differentiated tumors, offering clinical value for grading and treatment planning.