术前对于进展期鼻窦鳞状细胞癌（SNSCC）患者复发风险的预测对于个体化治疗至关重要。本研究旨在评估基于深度学习和多参数MRI的放射学特征（RS）对进展期SNSCC患者2年复发风险的预测能力。我们回顾性收集265名SNSCC患者（其中145人复发）的术前MRI数据集，包括T2加权（T2W）、增强T1加权（T1c）序列和扩散加权（DW）序列。所有患者分为165个训练组和70个测试组。我们使用基于VB-Net的深度学习分割模型来分割术前MRI感兴趣区域（ROI），从自动分割的ROI中提取放射学特征。在与临床病理学预测因子相结合的基础上，应用最小绝对收缩和选择算子（LASSO）和逻辑回归（LR）进行特征选择和放射学分数构建，从而开发出诊断评分表并进行其性能评估。此外，我们使用X-title软件将患者分成高风险和低风险早期复发（ER）亚组。回顾每个亚组的无复发生存概率（RFS）。其中，放射学分数、T分期、组织学分级和Ki-67预测因子是独立预测因子。T2WI、T1c和ADC序列的分割模型在测试组中分别达到0.720、0.727和0.756的Dice系数。RS-T2、RS-T1c和RS-ADC是从单参数MRI中提取的。相反，RS-Combined（与T2WI、T1c和ADC特征相结合）则是从多参数MRI中获得并在测试组中达到了0.854（0.749-0.927）和74.3%（0.624-0.840）的曲线下面积和准确率。校准曲线和决策曲线分析（DCA）展示了其在临床实践中的价值。Kaplan-Meier分析表明，无复发生存率2年低风险患者显著高于高风险患者，在训练组和测试组中均达到了显著性（p <0.001）。基于多序列MRI的自动评分表有助于术前预测SNSCC患者的ER。 Copyright © 2023. Elsevier公司出版。

Preoperative prediction of the recurrence risk in patients with advanced sinonasal squamous cell carcinoma (SNSCC) is critical for individualized treatment. To evaluate the predictive ability of radiomics signature (RS) based on deep learning and multiparametric MRI for the risk of 2-year recurrence in advanced SNSCC.Preoperative MRI datasets were retrospectively collected from 265 SNSCC patients (145 recurrences) who underwent preoperative MRI, including T2-weighted (T2W), contrast-enhanced T1-weighted (T1c) sequences and diffusion-weighted (DW). All patients were divided into 165 training cohort and 70 test cohort. A deep learning segmentation model based on VB-Net was used to segment regions of interest (ROIs) for preoperative MRI and radiomics features were extracted from automatically segmented ROIs. Least absolute shrinkage and selection operator (LASSO) and logistic regression (LR) were applied for feature selection and radiomics score construction. Combined with meaningful clinicopathological predictors, a nomogram was developed and its performance was evaluated. In addition, X-title software was used to divide patients into high-risk or low-risk early relapse (ER) subgroups. Recurrence-free survival probability (RFS) was assessed for each subgroup.The radiomics score, T stage, histological grade and Ki-67 predictors were independent predictors. The segmentation models of T2WI, T1c, and apparent diffusion coefficient (ADC) sequences achieved Dice coefficients of 0.720, 0.727, and 0.756, respectively, in the test cohort. RS-T2, RS-T1c and RS-ADC were derived from single-parameter MRI. RS-Combined (combined with T2WI, T1c, and ADC features) was derived from multiparametric MRI and reached area under curve (AUC) and accuracy of 0.854 (0.749-0.927) and 74.3% (0.624-0.840), respectively, in the test cohort. The calibration curve and decision curve analysis (DCA) illustrate its value in clinical practice. Kaplan-Meier analysis showed that the 2-year RFS rate for low-risk patients was significantly greater than that for high-risk patients in both the training and testing cohorts (p < 0.001).Automated nomograms based on multi-sequence MRI help to predict ER in SNSCC patients preoperatively.Copyright © 2023. Published by Elsevier Inc.