鼻息肉（NP）和内翻乳头状瘤（IP）是两种常见的鼻肿块类型。它们的区分对于确定最佳手术策略和预测结果至关重要。因此，我们的目标是开发几种放射组学模型，以根据计算机断层扫描 (CT) 提取的放射组学特征来区分它们。共有 296 名鼻息肉或乳头状瘤患者纳入我们的研究。从非对比 CT 图像中提取放射组学特征。对于特征选择，使用了Boruta、随机森林和相关系数三种方法。我们选择三种模型，即SVM、朴素贝叶斯和XGBoost，对所选特征进行二元分类。数据通过十倍交叉验证进行了验证。然后，通过受试者工作特征（ROC）曲线和相关参数来评估模型的性能。在本研究中，模型的性能能力按以下顺序排列：XGBoost > SVM > 朴素贝叶斯。 XGBoost 模型在四种条件（无特征选择、Boruta、随机森林和相关系数）下显示出优异的 AUC 性能，分别为 0.922、0.9078、0.9184 和 0.9141。我们证明了基于 CT 的放射组学在区分 IP 和 IP 方面发挥着至关重要的作用。 NP。它可以通过区分良性鼻部病变并减少侵入性诊断程序的需要来提供额外的诊断价值，并且可能在指导个性化治疗策略和制定最佳疗法方面发挥至关重要的作用。基于从平扫CT中提取肿瘤区域的放射组学特征通过放射组学优化，实现IP和NP的无创分类，为IP和NP的各自治疗提供支持。 • CT图像常用于诊断IP和NP。 • 放射组学在特征提取和分析方面表现出色。 • 基于CT 的放射组学可用于区分IP 和NP。 • 使用多种特征选择方法和分类器模型。 • 源自真实临床案例，数据丰富。© 2023。作者。

Nasal polyp (NP) and inverted papilloma (IP) are two common types of nasal masses. And their differentiation is essential for determining optimal surgical strategies and predicting outcomes. Thus, we aimed to develop several radiomic models to differentiate them based on computed tomography (CT)-extracted radiomic features.A total of 296 patients with nasal polyps or papillomas were enrolled in our study. Radiomics features were extracted from non-contrast CT images. For feature selection, three methods including Boruta, random forest, and correlation coefficient were used. We choose three models, namely SVM, naive Bayes, and XGBoost, to perform binary classification on the selected features. And the data was validated with tenfold cross-validation. Then, the performance was assessed by receiver operator characteristic (ROC) curve and related parameters.In this study, the performance ability of the models was in the following order: XGBoost > SVM > Naive Bayes. And the XGBoost model showed excellent AUC performance at 0.922, 0.9078, 0.9184, and 0.9141 under four conditions (no feature selection, Boruta, random forest, and correlation coefficient).We demonstrated that CT-based radiomics plays a crucial role in distinguishing IP from NP. It can provide added diagnostic value by distinguishing benign nasal lesions and reducing the need for invasive diagnostic procedures and may play a vital role in guiding personalized treatment strategies and developing optimal therapies.Based on the extraction of radiomic features of tumor regions from non-contrast CT, optimized by radiomics to achieve non-invasive classification of IP and NP which provide support for respective therapy of IP and NP.• CT images are commonly used to diagnose IP and NP. • Radiomics excels in feature extraction and analysis. • CT-based radiomics can be applied to distinguish IP from NP. • Use multiple feature selection methods and classifier models. • Derived from real clinical cases with abundant data.© 2023. The Author(s).