Machine learning–based radiomics for histological classification of parotid tumors using morphological MRI: a comparative study-Reference-Cited by-同舟云学术

Machine learning–based radiomics for histological classification of parotid tumors using morphological MRI: a comparative study

Published:2022-06-24 Issue:12 Volume:32 Page:8099-8110
ISSN:1432-1084
Container-title:European Radiology
language:en
Short-container-title:Eur Radiol

Author:

He Zhiying,Mao Yitao,Lu Shanhong,Tan Lei,Xiao Juxiong,Tan Pingqing,Zhang Hailin,Li Guo,Yan Helei,Tan Jiaqi,Huang Donghai,Qiu Yuanzheng,Zhang Xin,Wang Xingwei,Liu Yong

Funder

National Natural Science Foundation of China

Natural Science Foundation of Hunan Province

Huxiang Young Talent Project

Young Scientist Research Fund of Xiangya Hospital

National Key Research and Development Program of China

Publisher

Springer Science and Business Media LLC

Subject

Radiology, Nuclear Medicine and imaging,General Medicine

Link

https://link.springer.com/content/pdf/10.1007/s00330-022-08943-9.pdf

Reference59 articles.

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4. Liu CC, Jethwa AR, Khariwala SS, Johnson J, Shin JJ (2016) Sensitivity, specificity, and posttest probability of parotid fine-needle aspiration: a systematic review and meta-analysis. Otolaryngol Head Neck Surg 154:9–23

5. Singh Nanda KD, Mehta A, Nanda J (2012) Fine-needle aspiration cytology: a reliable tool in the diagnosis of salivary gland lesions. J Oral Pathol Med 41:106–112

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