Prediction of unenhanced lesion evolution in multiple sclerosis using radiomics-based models: a machine learning approach-Reference-Cited by-同舟云学术

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Prediction of unenhanced lesion evolution in multiple sclerosis using radiomics-based models: a machine learning approach

Published:2021-08 Issue: Volume:53 Page:102989
ISSN:2211-0348
Container-title:Multiple Sclerosis and Related Disorders
language:en
Short-container-title:Multiple Sclerosis and Related Disorders

Author:

Peng Yuling,Zheng Yineng,Tan Zeyun,Liu Junhang,Xiang Yayun,Liu Huan,Dai Linquan,Xie Yanjun,Wang Jingjie,Zeng Chun,Li Yongmei

Funder

National Natural Science Foundation of China

Basic Research and Frontier Exploration Project of Chongqing

Publisher

Elsevier BV

Subject

Clinical Neurology,Neurology,General Medicine

Reference28 articles.

1. Association of chronic active multiple sclerosis lesions with disability in vivo;Absinta;JAMA Neurol.,2019

2. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach;Aerts;Nat. Commun.,2014

3. Texture analysis in susceptibility-weighted imaging may be useful to differentiate acute from chronic multiple sclerosis lesions;Caruana;Eur. Radiol.,2020

4. Support-vector networks;Cortes;J. Mach. Learn.,1995

5. Slow expansion of multiple sclerosis iron rim lesions: pathology and 7 T magnetic resonance imaging;Dal-Bianco;Acta Neuropathol.,2017

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