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基于 MRI 的放射组学列线图开发和验证,用于鉴别腮腺沃辛瘤与多形性腺瘤。

Development and validation of an MRI-based radiomics nomogram for distinguishing Warthin's tumour from pleomorphic adenomas of the parotid gland.

机构信息

Health Management Center, The Affiliated Hospital of Qingdao University, Qingdao, China.

Department of Radiology, Yantai Yuhuangding Hospital, Yantai, China.

出版信息

Dentomaxillofac Radiol. 2021 Oct 1;50(7):20210023. doi: 10.1259/dmfr.20210023. Epub 2021 May 5.

Abstract

OBJECTIVE

: Preoperative differentiation between parotid Warthin's tumor (WT) and pleomorphic adenoma (PMA) is crucial for treatment decisions. The purpose of this study was to establish and validate an MRI-based radiomics nomogram for preoperative differentiation between WT and PMA.

METHODS AND MATERIALS

A total of 127 patients with histological diagnosis of WT or PMA from two clinical centres were enrolled in training set ( = 75; WT = 34, PMA = 41) and external test set ( = 52; WT = 24, PMA = 28). Radiomics features were extracted from axial T1WI and fs-T2WI images. A radiomics signature was constructed, and a radiomics score (Rad-score) was calculated. A clinical factors model was built using demographics and MRI findings. A radiomics nomogram combining the independent clinical factors and Rad-score was constructed. The receiver operating characteristic analysis was used to assess the performance levels of the nomogram, radiomics signature and clinical model.

RESULTS

The radiomics nomogram incorporating the age and radiomics signature showed favourable predictive value for differentiating parotid WT from PMA, with AUCs of 0.953 and 0.918 for the training set and test set, respectively.

CONCLUSIONS

The MRI-based radiomics nomogram had good performance in distinguishing parotid WT from PMA, which could optimize clinical decision-making.

摘要

目的

术前区分腮腺沃辛瘤(WT)和多形性腺瘤(PMA)对于治疗决策至关重要。本研究旨在建立和验证一种基于 MRI 的放射组学列线图,用于术前区分 WT 和 PMA。

方法和材料

本研究共纳入了来自两个临床中心的经组织学诊断为 WT 或 PMA 的 127 名患者,包括训练集( = 75;WT = 34,PMA = 41)和外部测试集( = 52;WT = 24,PMA = 28)。从轴向 T1WI 和 fs-T2WI 图像中提取放射组学特征。构建放射组学特征,并计算放射组学评分(Rad-score)。使用人口统计学和 MRI 结果构建临床因素模型。构建结合独立临床因素和 Rad-score 的放射组学列线图。采用受试者工作特征分析评估列线图、放射组学特征和临床模型的性能水平。

结果

纳入年龄和放射组学特征的放射组学列线图在区分腮腺 WT 和 PMA 方面表现出良好的预测价值,训练集和测试集的 AUC 分别为 0.953 和 0.918。

结论

基于 MRI 的放射组学列线图在区分腮腺 WT 和 PMA 方面具有良好的性能,可优化临床决策。

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