人工智能辅助的CRSwNP 2型炎症内型分型：从鼻窦CT和数字病理到生物制剂决策支持。

文献信息

中文摘要

背景： 慢性鼻窦炎伴鼻息肉（CRSwNP）是一种异质性鼻科疾病，常由2型炎症驱动，然而常规临床表型分析仍不足以支持基于内型的治疗管理。常规标志物，包括血嗜酸性粒细胞、血清免疫球蛋白E（IgE）、呼出气一氧化氮分数、组织嗜酸性粒细胞增多、鼻息肉评分和鼻窦计算机断层扫描（CT）评分，提供的信息有用但不完整。人工智能（AI）能够整合鼻窦CT、影像组学特征、数字病理、生物标志物、合并症和随访治疗结局。新兴研究表明，基于CT的AI支持无创识别嗜酸性或2型高疾病，而数字病理将常规组织学与炎症和分子内型联系起来。然而，已发表证据仍以回顾性单模态研究或部分多源整合为主，尚无模型在CRSwNP中联合整合鼻窦CT、数字全切片病理和生物标志物。其他局限性包括内型标签异质性、外部验证不足以及生物制剂决策的临床效用不确定。本小型综述综合了现有的单模态人工智能流程，将CRSwNP作为AI驱动的2型炎症内型分型的代表性疾病模型加以情境化，并提出一个面向临床的多模态可解释评分框架，整合鼻窦影像和数字病理，以支持CRS管理中的精准2型分层。

英文摘要

Chronic rhinosinusitis with nasal polyps (CRSwNP) is a heterogeneous rhinologic disease frequently driven by type 2 inflammation, yet routine clinical phenotyping remains insufficient for endotype-informed management. Conventional markers, including blood eosinophils, serum immunoglobulin E (IgE), fractional exhaled nitric oxide, tissue eosinophilia, nasal polyp score, and sinus computed tomography (CT) scores, provide useful but incomplete information. Artificial intelligence (AI) enables integration of sinus CT, radiomic features, digital pathology, biomarkers, comorbidities, and follow-up treatment outcomes. Emerging studies suggest that CT-based AI supports non-invasive identification of eosinophilic or type 2-high disease, whereas digital pathology links routine histology with inflammatory and molecular endotypes. However, published evidence remains dominated by retrospective single-modality studies or partial multi-source integration, and no model has yet jointly integrated sinus CT, digital whole-slide pathology, and biomarkers in CRSwNP. Additional limitations include heterogeneous endotype labels, insufficient external validation, and uncertain clinical utility for biologic decision-making. This Mini Review synthesizes existing single-modality artificial intelligence pipelines, contextualizes CRSwNP as a representative disease model for AI-driven type 2 inflammatory endotyping, and proposes a clinically oriented, multimodal interpretable scoring framework integrating sinus imaging and digital pathology to support precision type 2 stratification in CRS management.