鼻窦鳞状细胞癌（SCC）预后较差，局部复发的可能性较高。我们的目的是评估 MRI 放射组学是否可以预测鼻窦 SCC 的早期局部失败。连续招募了 68 名淋巴结阴性鼻窦 SCC 患者（2005 年 1 月至 2020 年 12 月），分配到训练集（n = 47）和测试集（n = 47）。 n = 21）。早期局部失败是主要终点，发生在初始治疗完成后 12 个月内。对于临床特征（年龄、部位、治疗方式和临床 T 分期），进行二元逻辑回归分析。对于提取的186个放射组学特征，结合不同的特征选择和分类器创建两个预测模型：（1）纯放射组学模型； (2)具有临床特征和放射组学的组合模型。使用 DeLong 方法计算并比较接受者操作特征曲线下的面积 (AUC)。训练集和测试集的早期局部失败率分别为 38.3% (18/47) 和 23.8% (5/21)。我们发现了一些与早期局部失败密切相关的放射组学特征。在测试集中，与临床模型相比，表现最佳的放射组学模型和组合模型（临床放射组学特征）均产生了更高的 AUC（AUC，0.838 vs. 0.438，p = 0.020；0.850 vs. 0.438，p = 0.016，分别）。表现最佳的放射组学模型和组合模型的性能没有显着差异（AUC，0.838 vs. 0.850，p = 0.904）。与机器学习分类器集成的 MRI 放射组学可以预测鼻窦 SCC 患者的早期局部失败。MRI与机器学习分类器相结合的放射组学可以比临床模型更准确地预测鼻窦鳞状细胞癌的早期局部失败。• 确定了与鼻窦鳞状细胞癌患者的早期局部失败显着相关的放射组学特征子集。 • MRI 放射组学与机器学习分类器集成可以高精度预测早期局部失败，这在测试集中得到了验证（曲线下面积 = 0.838）。 • 与放射组学相比，临床和放射组学组合模型在早期局部失败预测方面具有优越的性能（测试集中曲线下面积为 0.850 与 0.838），且没有统计学上的显着差异。© 2023。作者，获得欧洲放射学会的独家许可。

Sinonasal squamous cell carcinoma (SCC) follows a poor prognosis with high tendency for local recurrence. We aimed to evaluate whether MRI radiomics can predict early local failure in sinonasal SCC.Sixty-eight consecutive patients with node-negative sinonasal SCC (January 2005-December 2020) were enrolled, allocated to the training (n = 47) and test sets (n = 21). Early local failure, which occurred within 12 months of completion of initial treatment, was the primary endpoint. For clinical features (age, location, treatment modality, and clinical T stage), binary logistic regression analysis was performed. For 186 extracted radiomic features, different feature selections and classifiers were combined to create two prediction models: (1) a pure radiomics model; and (2) a combined model with clinical features and radiomics. The areas under the receiver operating characteristic curves (AUCs) were calculated and compared using DeLong's method.Early local failure occurred in 38.3% (18/47) and 23.8% (5/21) in the training and test sets, respectively. We identified several radiomic features which were strongly associated with early local failure. In the test set, both the best-performing radiomics model and the combined model (clinical + radiomic features) yielded higher AUCs compared to the clinical model (AUC, 0.838 vs. 0.438, p = 0.020; 0.850 vs. 0.438, p = 0.016, respectively). The performances of the best-performing radiomics model and the combined model did not differ significantly (AUC, 0.838 vs. 0.850, p = 0.904).MRI radiomics integrated with a machine learning classifier may predict early local failure in patients with sinonasal SCC.MRI radiomics intergrated with machine learning classifiers may predict early local failure in sinonasal squamous cell carcinomas more accurately than the clinical model.• A subset of radiomic features which showed significant association with early local failure in patients with sinonasal squamous cell carcinomas was identified. • MRI radiomics integrated with machine learning classifiers can predict early local failure with high accuracy, which was validated in the test set (area under the curve = 0.838). • The combined clinical and radiomics model yielded superior performance for early local failure prediction compared to that of the radiomics (area under the curve 0.850 vs. 0.838 in the test set), without a statistically significant difference.© 2023. The Author(s), under exclusive licence to European Society of Radiology.