Radiomics-based unsupervised clustering and deep learning nomogram for response prediction after neoadjuvant chemotherapy in locally advanced laryngeal cancer

Objectives To develop a nomogram model for individualized prediction of neoadjuvant chemotherapy (NAC) response in locally advanced laryngeal cancer (LALC).Methods A total of 175 patients who underwent CT examinations before NAC from two hospitals were retrospectively enrolled and divided into training (n = 112) and test (n = 63) sets. Significant radiomics and clinical features were selected sequentially, and consensus clustering was used to identify tumor subtypes. A nomogram model for predicting remission status was constructed by fusing clinical signature, radiomics signature, cluster result and deep learning signature. The predictive performance of junior and senior doctors for NAC response was evaluated with and without model assistance.Results Two radiomics subtypes (Subtype A and B) were identified. Compared with Subtype A, Subtype B had higher frequencies of heterogeneous tumor density (all P Conclusion The nomogram model based on two-center databases achieved good performance in predicting NAC response and survival in LALC patients, which may contribute to the personalized treatment of LALC.