Multi-channel deep learning radiomics model based on contrast-enhanced CT for predicting postoperative prognosis in...

Accurate prediction of prognosis and risk stratification in patients with laryngeal cancer can inform appropriate treatment decision-making. This study aims to develop a multi-channel deep learning radiomics model based on contrast-enhanced computed tomography (CECT) for predicting postoperative overall survival (OS) in patients.

A total of 272 patients with laryngeal cancer were retrospectively recruited from two hospitals between January 2016 and July 2021. Specifically, 156 patients were enrolled from Center 1 as the training cohort, and 116 patients from Center 2 as the external test cohort. Two imaging signatures, reflecting phenotypes of the radiomics and multi-channel deep learning features, were constructed using pretreatment venous-phase CECT images. Feature selection involved reproducibility evaluation, Spearman correlation coefficient, and least absolute shrinkage and selection operator (LASSO) regression. Ten machine learning algorithms were employed to construct the signatures. An integrated Deep Learning Radiomics Nomogram (DLRN) was subsequently developed to predict OS. Predictive performance was evaluated using the area under the curve (AUC), calibration curves, and decision curve analysis (DCA). Kaplan-Meier survival curves derived from the DLRN were used for patient risk stratification, and subgroup analyses were conducted to validate the model's robustness.

The DLRN demonstrated satisfactory prognostic performance for laryngeal cancer. In the external test cohort, the 1-, 2-, and 3-year AUC values were 0.74, 0.75, and 0.80, respectively, and achieved a C-index of 0.73, outperforming individual models. Calibration curves and DCA indicated excellent calibration and the highest clinical net benefit. Subgroup analyses confirmed consistent DLRN performance across clinical stages, age groups, and surgical modalities.

The proposed multi-channel deep learning radiomics model showed promising performance for predicting OS in laryngeal cancer patients. This approach may support individualized risk stratification and assist clinical decision-making in patients with laryngeal carcinoma.