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

内容摘要

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. 目的： 利用基于鼻窦CT的多任务学习网络（multitask learning network，MTLNet）构建并外部验证慢性鼻窦炎伴鼻息肉（CRSwNP）患者内在型的预测模型。 方法： 回顾性收集2020年1月1日至2024年4月30日在汕头大学医学院第二附属医院进行初次治疗的CRSwNP患者，按8∶2的比例随机分为训练集与验证集；回顾性收集同医院2024年5月1日至11月30日的患者作为外部测试集。根据《慢性鼻窦炎诊断与治疗指南（2024）》将内在型分为嗜酸粒细胞型和非嗜酸粒细胞型。MTLNet模型采用U型架构，可同时输出三维鼻窦区域分割和内在型分类两个任务。通过Dice相似系数（Dice similarity coefficient，DSC）、混淆矩阵和曲线下面积（area under the curve，AUC）评估模型预测性能。采用三维图像重建技术和梯度加权类激活映射（gradient-weighted class activation mapping，Grad-CAM）可视化解释模型工作原理。 结果： 共纳入257例CRSwNP患者，其中训练集172例、验证集41例、外部测试集44例。在训练集和验证集中，MTLNet模型具有良好的三维鼻窦区域分割性能（DSC分别为0.913和0.887）和内在型分类性能（AUC分别为0.871和0.770）；在外部测试集中，MTLNet模型的预测效能仍表现良好：分割DSC为0.898，内在型分类AUC为0.818（灵敏度72.7%，特异度78.8%），说明模型具有良好的泛化能力。三维图像重建技术及Grad-CAM可视化显示模型具有良好的可解释性。 结论： 本研究开发了一种新型的MTLNet模型，且具有良好的临床预测效能，实现了人工智能赋能的CRSwNP内在型准确预测，可协助鼻科医生制订个体化精准治疗策略。.