基于CT的深度学习用于识别鼻腔鼻窦内翻性乳头状瘤：一项单中心回顾性研究
CT-based deep learning for identifying sinonasal inverted papilloma: a single-center retrospective study.
文献信息
| PMID | 42830354 |
|---|---|
| 原文 | 在 PubMed 查看原文 ↗ |
| 发表日期 | 2026 |
| 作者 | Günay Kozan |
| 作者单位 | Department of Otorhinolaryngology and Head and Neck Surgery Clinic, Dicle University Faculty of Medicine, Diyarbakır, 21010, Turkey. gunaykozan@hotmail.com. |
| 期刊 | European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery |
| SCI 分区 | Q1 |
| IF | 2.3 |
| 研究类型 | AI/ML · 临床 |
| 所属专科 | 鼻科 |
中文摘要
背景： 本研究旨在开发一种基于CT的深度学习模型，作为决策支持系统，用于对鼻腔鼻窦内翻性乳头状瘤（IP）、鼻息肉和正常鼻窦解剖结构进行分类。
方法： 分析了2020年至2024年间接受鼻窦CT成像的871例患者的回顾性数据集。所有诊断均经组织病理学证实。由一位对最终诊断不知情的经验丰富的放射科医生在患者层面选择每位患者的两张代表性CT切片。数据集按70%训练集和30%测试集划分，其中20%的训练数据用于验证。未纳入任何临床变量；模型仅依赖影像数据。训练并比较了基于ResNet50的卷积神经网络和Vision Transformer模型。
结果： ResNet50模型的测试准确率为98.47%，优于Vision Transformer模型（79.65%）。该模型表现出较高的各类别性能，尤其是在内翻性乳头状瘤检测方面。
结论： 基于CT的深度学习模型可作为鼻腔鼻窦内翻性乳头状瘤分类的决策支持工具。该方法并非取代组织病理学，而是可能有助于临床分诊，并在混合诊断策略中补充传统诊断流程。
英文摘要
BACKGROUND: This study aimed to develop a CT-based deep learning model as a decision-support system for the classification of sinonasal inverted papilloma (IP), nasal polyps, and normal paranasal anatomy.
METHODS: A retrospective dataset of 871 patients who underwent paranasal CT imaging between 2020 and 2024 was analyzed. All diagnoses were confirmed histopathologically. Two representative CT slices per patient were selected at the patient level by an experienced radiologist blinded to final diagnosis. The dataset was split into 70% training and 30% testing sets, with 20% of the training data used for validation. No clinical variables were included; the model relied solely on imaging data. A ResNet50-based convolutional neural network and a Vision Transformer model were trained and compared.
RESULTS: The ResNet50 model achieved a test accuracy of 98.47%, outperforming the Vision Transformer model (79.65%). The model demonstrated high class-wise performance, particularly for inverted papilloma detection.
CONCLUSION: A CT-based deep learning model can serve as a decision-support tool for the classification of sinonasal inverted papilloma. Rather than replacing histopathology, this approach may assist clinical triage and complement conventional diagnostic workflows in a hybrid diagnostic strategy.