Corrigendum: Radiomic-Based Quantitative CT Analysis of Pure Ground-Glass Nodules to Predict the Invasiveness of Lung Adenocarcinoma

Corrigendum: Radiomic-Based Quantitative CT Analysis of Pure Ground-Glass Nodules to Predict the Invasiveness of Lung Adenocarcinoma

Bibliographic Details
Main Authors: Fangyi Xu, Wenchao Zhu, Yao Shen, Jian Wang, Rui Xu, Chooah Outesh, Lijiang Song, Yi Gan, Cailing Pu, Hongjie Hu
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-10-01
Series:Frontiers in Oncology
Subjects:
radiomics
lung cancer
adenocarcinoma
computed tomography
machine learning
Online Access:https://www.frontiersin.org/articles/10.3389/fonc.2020.608365/full
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spelling doaj-69784bc997c44bba953e5775465916562020-11-25T02:20:40ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2020-10-011010.3389/fonc.2020.608365608365Corrigendum: Radiomic-Based Quantitative CT Analysis of Pure Ground-Glass Nodules to Predict the Invasiveness of Lung AdenocarcinomaFangyi Xu0Wenchao Zhu1Yao Shen2Yao Shen3Jian Wang4Rui Xu5Rui Xu6Chooah Outesh7Lijiang Song8Yi Gan9Cailing Pu10Hongjie Hu11Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaDepartment of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaDepartment of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaDepartment of Radiology, Yinzhou Hospital Affiliated With the School of Medicine of Ningbo University, Ningbo, ChinaDepartment of Radiology, Tongde Hospital of Zhejiang Province, Hangzhou, ChinaDUT-RU International School of Information Science & Engineering, Dalian University of Technology, Dalian, ChinaDUT-RU Co-Research Center of Advanced ICT for Active Life, Dalian, ChinaDepartment of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaDepartment of Cardiothoracic Surgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaDepartment of Pathology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaDepartment of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaDepartment of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Chinahttps://www.frontiersin.org/articles/10.3389/fonc.2020.608365/fullradiomicslung canceradenocarcinomacomputed tomographymachine learning
collection DOAJ
language English
format Article
sources DOAJ
author Fangyi Xu
Wenchao Zhu
Yao Shen
Yao Shen
Jian Wang
Rui Xu
Rui Xu
Chooah Outesh
Lijiang Song
Yi Gan
Cailing Pu
Hongjie Hu
spellingShingle Fangyi Xu
Wenchao Zhu
Yao Shen
Yao Shen
Jian Wang
Rui Xu
Rui Xu
Chooah Outesh
Lijiang Song
Yi Gan
Cailing Pu
Hongjie Hu
Corrigendum: Radiomic-Based Quantitative CT Analysis of Pure Ground-Glass Nodules to Predict the Invasiveness of Lung Adenocarcinoma
Frontiers in Oncology
radiomics
lung cancer
adenocarcinoma
computed tomography
machine learning
author_facet Fangyi Xu
Wenchao Zhu
Yao Shen
Yao Shen
Jian Wang
Rui Xu
Rui Xu
Chooah Outesh
Lijiang Song
Yi Gan
Cailing Pu
Hongjie Hu
author_sort Fangyi Xu
title Corrigendum: Radiomic-Based Quantitative CT Analysis of Pure Ground-Glass Nodules to Predict the Invasiveness of Lung Adenocarcinoma
title_short Corrigendum: Radiomic-Based Quantitative CT Analysis of Pure Ground-Glass Nodules to Predict the Invasiveness of Lung Adenocarcinoma
title_full Corrigendum: Radiomic-Based Quantitative CT Analysis of Pure Ground-Glass Nodules to Predict the Invasiveness of Lung Adenocarcinoma
title_fullStr Corrigendum: Radiomic-Based Quantitative CT Analysis of Pure Ground-Glass Nodules to Predict the Invasiveness of Lung Adenocarcinoma
title_full_unstemmed Corrigendum: Radiomic-Based Quantitative CT Analysis of Pure Ground-Glass Nodules to Predict the Invasiveness of Lung Adenocarcinoma
title_sort corrigendum: radiomic-based quantitative ct analysis of pure ground-glass nodules to predict the invasiveness of lung adenocarcinoma
publisher Frontiers Media S.A.
series Frontiers in Oncology
issn 2234-943X
publishDate 2020-10-01
topic radiomics
lung cancer
adenocarcinoma
computed tomography
machine learning
url https://www.frontiersin.org/articles/10.3389/fonc.2020.608365/full
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