Dual-energy CT may predict post-operative recurrence in early-stage glottic laryngeal cancer: a novel nomogram and risk stratification system.

Objectives: To establish and validate a predictive model integrating with clinical and dual-energy CT (DECT) variables for individual recurrence-free survival (RFS) prediction in early-stage glottic laryngeal cancer (EGLC) after larynx-preserving surgery. Methods: This retrospective study included 212 consecutive patients with EGLC who underwent DECT before larynx-preserving surgery between January 2015 and December 2018. Using Cox proportional hazard regression model to determine independent predictors for RFS and presented on a nomogram. The model's performance was assessed using Harrell's concordance index (C-index), time-dependent area under curve (TD-AUC) plot, and calibration curve. A risk stratification system was established using the nomogram with median scores of all cases to divide all patients into two prognostic groups. Results: Recurrence occurred in 39/212 (18.4%) cases. Normalized iodine concentration in arterial (NICAP) and venous phases (NICVP) were verified as significant predictors of RFS in multivariate Cox regression (hazard ratio [HR], 4.2; 95% confidence interval [CI]: 2.3, 7.7, p <.001 and HR, 3.0; 95% CI: 1.5, 5.9, p =.002, respectively). Nomogram based on clinical and DECT variables was better than did only clinical variables. The prediction model proved well-calibrated and had good discriminative ability in the training and validation samples. A risk stratification system was built that could effectively classify EGLC patients into two risk groups. Conclusions: DECT could provide independent RFS indicators in patients with EGLC, and the nomogram based on DECT and clinical variables was useful in predicting RFS at several time points. Key Points: • Dual-energy CT(DECT) variables can predict recurrence-free survival (RFS) after larynx-preserving surgery in patients with early-stage glottic laryngeal cancer (EGLC). • The model that integrates clinical and DECT variables predicted RFS better than did only clinical variables. • A risk stratification system based on the nomogram could effectively classify EGLC patients into two risk groups. [ABSTRACT FROM AUTHOR]