Predicting muscle-invasive bladder cancer with dual-layer detector spectral CT-derived extracellular volume fraction.

OBJECTIVES

To evaluate the diagnostic performance of dual-layer detector spectral CT (DLCT)-derived extracellular volume fraction (ECV) for pre-operatively distinguishing muscle-invasive bladder cancer (MIBC) from non-muscle-invasive bladder cancer (NMIBC). MATERIALS AND

METHODS

This retrospective study included 116 patients with pathologically confirmed urothelial carcinoma who underwent preoperative DLCT. Pathological results from transurethral resection or cystectomy served as the reference standard, classifying patients into MIBC (n = 57) and NMIBC (n = 59) groups. Two radiologists independently measured morphological features and spectral CT parameters, including iodine density and ECV. Statistical analyses involved univariate comparisons, multivariable logistic regression, and receiver operating characteristic (ROC) analysis.

RESULTS

Multivariable analysis identified the longest tumor contact length (CL) ≥ 3 cm (Odds Ratio [OR] = 5.827; p = 0.006) and ECV (OR = 1.378 per 1% increment; p < 0.001) as independent predictors of MIBC. The area under the ROC curve (AUC) for ECV (0.886) was significantly superior to that of CL ≥ 3 cm (0.726) (DeLong test, p = 0.040). A combined model (ECV + CL) achieved an AUC of 0.869, which was not significantly better than ECV alone (p = 0.146). At the optimal cut-off of 72.3%, ECV predicted MIBC with 84.2% sensitivity and 88.1% specificity. Excellent inter-reader agreement was observed for all quantitative measurements (ICC ≥ 0.86).

CONCLUSION

DLCT-derived ECV is a robust, non-invasive, and reproducible quantitative biomarker that outperforms conventional morphological assessment for the pre-operative prediction of MIBC, offering significant potential for improving clinical decision-making and personalized treatment planning.