Preoperative Prediction of Perineural Invasion and Survival in Gastric Cancer Using Extracellular Volume Fraction and Dual-Energy CT Quantitative Parameters: A Dual-Center Study.

RATIONALE AND

OBJECTIVES

This study aimed to develop a nomogram integrating extracellular volume fraction (ECV), dual-energy CT (DECT) quantitative parameters, and morphological features to predict perineural invasion (PNI) and recurrence-free survival (RFS) in gastric cancer (GC).

MATERIALS AND

METHODS

We retrospectively collected GC patients' data from two centers. Two radiologists independently assessed ECV, DECT quantitative parameters, and morphological features. Multivariate logistic regression analyses were performed to identify independent risk factors for PNI and construct a predictive nomogram. The nomogram's predictive performance was evaluated using calibration curves, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA). Multivariate Cox regression analyses were conducted to determine independent prognostic factors for RFS. Kaplan-Meier survival curves were generated to compare RFS between nomogram predicted PNI-positive and PNI-negative groups.

RESULTS

A total of 268 patients were included in the analysis, with 166 in the training cohort and 102 in the validation cohort. ECV, NICdelay, and ctEMVI were identified as independent risk factors for PNI. The nomogram demonstrated good predictive performance for PNI, with area under the ROC curve (AUC) of 0.822 and 0.810 in the training and validation cohorts. Calibration curves indicated good agreement between predicted and observed PNI, and DCA demonstrated clinical utility. Nomogram-predicted PNI was an independent prognostic factor for RFS, with the predicted PNI-positive group exhibiting significantly lower RFS rate than the predicted PNI-negative group.

CONCLUSION

A nomogram integrating ECV, DECT quantitative parameters, and morphological features could effectively predict PNI in GC and provide significant prognostic value for postoperative RFS.