本研究旨在在大样本、多中心数据集上利用基于 MR 放射组学的机器学习分类器来开发预测恶性鼻窦肿瘤和肿瘤样病变的最佳模型。这项研究包括 1711 名成年患者（875 名良性和 836 名恶性），鼻窦肿瘤或肿瘤样病变来自三个机构。来自机构 1 的患者 (n = 1367) 构成训练和验证队列，而来自机构 2 和 3 的患者 (n = 158/186) 构成测试队列。在T1WI、T2WI和对比增强T1WI（CE-T1WI）上对肿瘤感兴趣区域进行手动分割。使用十个机器学习分类器执行数据标准化、降维、特征选择和分类。利用T1WI、T2WI、CE最优模型中对特征选择贡献最大的前10个特征，构建了T1WI  T2WI、T1WI CE-T1WI、T2WI  CE-T1WI、T1WI  T2WI  CE-T1WI四种融合模型-T1WI。 Delong检验比较了模型之间的曲线下面积（AUC）。T1WI、T2WI、CE-T1WI的训练/验证/test1/test2数据集的AUC分别为0.900/0.842/0.872/0.839、0.876/0.789/0.8​​42/0.863 、 和 0.899/0.8​​24/0.831/0.707。 T1WI  T2WI  CE-T1WI的融合模型具有最高的AUC。训练/验证/test1/test2数据集的AUC为0.947/0.849/0.8​​71/0.887。在两个队列中，T1WI  T2WI  CE-T1WI 模型的 AUC 均显着高于 T2WI  CE-T1WI 模型 (p < 0.05)，并且优于测试 1 中的 T2WI 模型 (p = 0.008) 和测试 2 中的 T1WI 模型 (p = 0.006)。这种基于T1WI  T2WI  CE-T1WI图像的放射组学和机器学习的融合模型可以提高预测恶性鼻窦肿瘤的能力，具有高精度、弹性和鲁棒性。我们的研究提出了一种基于T1放射组学的机器学习融合模型-和T2加权图像和增强T1加权图像，可以无创地识别鼻窦肿瘤的性质，提高预测恶性鼻窦肿瘤的性能。由于临床表现相似，区分良性和恶性鼻窦肿瘤很困难。 T1  T2 对比增强 T1 图像的放射组学模型可以识别鼻窦肿瘤的性质。该模型可以帮助区分良性和恶性鼻窦肿瘤。© 2024。作者，获得欧洲放射学会的独家许可。

This study aimed to utilize MR radiomics-based machine learning classifiers on a large-sample, multicenter dataset to develop an optimal model for predicting malignant sinonasal tumors and tumor-like lesions.This study included 1711 adult patients (875 benign and 836 malignant) with sinonasal tumors or tumor-like lesions from three institutions. Patients from institution 1 (n = 1367) constituted both the training and validation cohorts, while those from institution 2 and 3 (n = 158/186) made up the test cohorts. Manual segmentation of the region of interest of the tumor was performed on T1WI, T2WI, and contrast-enhanced T1WI (CE-T1WI). Data normalization, dimensional reductions, feature selection, and classifications were performed using ten machine-learning classifiers. Four fusion models, namely T1WI + T2WI, T1WI + CE-T1WI, T2WI + CE-T1WI, and T1WI + T2WI + CE-T1WI, were constructed using the top ten features with the highest contribution in feature selection in the optimal models of T1WI, T2WI, and CE-T1WI. The Delong test compared areas under the curve (AUC) between models.The AUCs of training/validation/test1/test2 datasets for T1WI, T2WI, and CE-T1WI were 0.900/0.842/0.872/0.839, 0.876/0.789/0.842/0.863, and 0.899/0.824/0.831/0.707, respectively. The fusion model from T1WI + T2WI + CE-T1WI had the highest AUC. The AUCs of training/validation/test1/test2 datasets were 0.947/0.849/0.871/0.887. The T1WI + T2WI + CE-T1WI model demonstrated a significantly higher AUC than the T2WI + CE-T1WI model in both cohorts (p < 0.05) and outperformed the T2WI model in test 1 (p = 0.008) and the T1WI model in test 2 (p = 0.006).This fusion model based on radiomics from T1WI + T2WI + CE-T1WI images and machine learning can improve the power in predicting malignant sinonasal tumors with high accuracy, resilience, and robustness.Our study proposes a radiomics-based machine learning fusion model from T1- and T2-weighted images and contrast-enhanced T1-weighted images, which can non-invasively identify the nature of sinonasal tumors and improve the performance in predicting malignant sinonasal tumors.Differentiating benign and malignant sinonasal tumors is difficult due to similar clinical presentations. A radiomics model from T1 + T2 + contrast-enhanced T1 images can identify the nature of sinonasal tumors. This model can help distinguish benign and malignant sinonasal tumors.© 2024. The Author(s), under exclusive licence to European Society of Radiology.