[CT-based multitask learning for predicting endotypes of chronic rhinosinusitis with nasal polyps: a clinical study]

Objective: To construct and validate a predictive model for endotypes in patients with chronic rhinosinusitis with nasal polyps (CRSwNP) using a sinus CT-based multitask learning network (MTLNet). Methods: CRSwNP patients who underwent initial treatment at the Second Affiliated Hospital of Shantou University Medical College from January 1, 2020 to April 30, 2024 were retrospectively enrolled and randomly divided into training and validation sets in an 8∶2 ratio. Patients from May 1 to November 30, 2024 were retrospectively enrolled as the external validation set at the same center. Endotypes were classified into eosinophilic and non-eosinophilic types according to the Guideline for Diagnosis and Treatment of Chronic Rhinosinusitis (2024). The MTLNet model adopted a U-shaped architecture, capable of simultaneously performed two tasks: three-dimensional (3D) sinus region segmentation and endotype classification. Model performance was evaluated using Dice similarity coefficient (DSC), confusion matrices, and the area under the curve (AUC) with 95% confidence intervals (CIs) calculated via bootstrap resampling. 3D image reconstruction technology and gradient-weighted class activation mapping (Grad-CAM) were used for visual explanation of the model's working mechanism. Results: A total of 257 CRSwNP patients were included, including 172 in the training set, 41 in the validation set, and 44 in the external testing set. In the training and validation sets, the MTLNet model exhibited excellent 3D sinus region segmentation performance (DSC: 0.913 and 0.887, respectively) and endotype classification performance (AUC: 0.871 and 0.770, respectively). In the external test set, the model maintained good predictive performance with a segmentation DSC of 0.898 and an endotype classification AUC of 0.818 (sensitivity 72.7%, specificity 78.8%), indicating favorable generalization ability. 3D image reconstruction technology and Grad-CAM visualization demonstrated good model interpretability. Conclusion: A novel MTLNet model is developed with excellent clinical predictive performance, achieving artificial intelligence-enabled accurate CRSwNP endotype prediction that can assist rhinologists in formulating individualized and precise treatment strategies.