Spectral CT-based nomogram for preoperative prediction of perineural invasion in locally advanced gastric cancer: a prospective study

Objectives This work focused on developing and validating the spectral CT-based nomogram to preoperatively predict perineural invasion (PNI) for locally advanced gastric cancer (LAGC). Methods This work prospectively included 196 surgically resected LAGC patients (139 males, 57 females, 59.55 ± 11.97 years) undergoing triple enhanced spectral CT scans. Patients were labeled as perineural invasion (PNI) positive and negative according to pathologic reports, then further split into primary ( n = 130) and validation cohort ( n = 66). We extracted clinicopathological information, follow-up data, iodine concentration (IC), and normalized IC values against to aorta (nICs) at arterial/venous/delayed phases (AP/VP/DP). Clinicopathological features and IC values between PNI positive and negative groups were compared. Multivariable logistic regression was performed to screen independent risk factors of PNI. Then, a nomogram was established, and its capability was determined by ROC curves. Its clinical use was evaluated by decision curve analysis. The correlations of PNI and the nomogram with patients’ survival were explored by log-rank survival analysis. Results Borrmann classification, tumor thickness, and nICDP were independent predictors of PNI and used to build the nomogram. The nomogram yielded higher AUCs of 0.853 (0.744–0.928) and 0.782 (0.701–0.850) in primary and validation cohorts than any other parameters ( p