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基于超声的放射组学可对颈部淋巴结病的病因进行分类:一项多中心回顾性研究。

Ultrasound-Based Radiomics Can Classify the Etiology of Cervical Lymphadenopathy: A Multi-Center Retrospective Study.

作者信息

Liu Yajing, Chen Jifan, Zhang Chao, Li Qunying, Zhou Hang, Zeng Yiqing, Zhang Ying, Li Jia, Xv Wen, Li Wencun, Zhu Jianing, Zhao Yanan, Chen Qin, Huang Yi, Li Hongming, Huang Ying, Yang Gaoyi, Huang Pintong

机构信息

Department of Ultrasound in Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China.

Department of Ultrasound, Hangzhou Red Cross Hospital, Hangzhou, China.

出版信息

Front Oncol. 2022 May 17;12:856605. doi: 10.3389/fonc.2022.856605. eCollection 2022.

Abstract

Medical diagnostic imaging is essential for the differential diagnosis of cervical lymphadenopathy. Here we develop an ultrasound radiomics method for accurately differentiating cervical lymph node tuberculosis (LNTB), cervical lymphoma, reactive lymph node hyperplasia, and metastatic lymph nodes especially in the multi-operator, cross-machine, multicenter context. The inter-observer and intra-observer consistency of radiomics parameters from the region of interest were 0.8245 and 0.9228, respectively. The radiomics model showed good and repeatable diagnostic performance for multiple classification diagnosis of cervical lymphadenopathy, especially in LNTB (area under the curve, AUC: 0.673, 0.662, and 0.626) and cervical lymphoma (AUC: 0.623, 0.644, and 0.602) in the whole set, training set, and test set, respectively. However, the diagnostic performance of lymphadenopathy among skilled radiologists was varied (Kappa coefficient: 0.108, * < 0.001). The diagnostic performance of radiomics is comparable and more reproducible compared with those of skilled radiologists. Our study offers a more comprehensive method for differentiating LNTB, cervical lymphoma, reactive lymph node hyperplasia, and metastatic LN.

摘要

医学诊断成像对于颈部淋巴结病的鉴别诊断至关重要。在此,我们开发了一种超声放射组学方法,用于准确鉴别颈部淋巴结结核(LNTB)、颈部淋巴瘤、反应性淋巴结增生和转移性淋巴结,尤其是在多操作者、跨机器、多中心的情况下。来自感兴趣区域的放射组学参数的观察者间和观察者内一致性分别为0.8245和0.9228。放射组学模型在颈部淋巴结病的多分类诊断中显示出良好且可重复的诊断性能,尤其是在整个数据集、训练集和测试集中,对于LNTB(曲线下面积,AUC:0.673、0.662和0.626)和颈部淋巴瘤(AUC:0.623、0.644和0.602)。然而,熟练放射科医生对淋巴结病的诊断性能存在差异(Kappa系数:0.108,*<0.001)。与熟练放射科医生相比,放射组学的诊断性能相当且更具可重复性。我们的研究提供了一种更全面的方法来鉴别LNTB、颈部淋巴瘤、反应性淋巴结增生和转移性淋巴结。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc8e/9152112/a7920845c572/fonc-12-856605-g001.jpg

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