Please use this identifier to cite or link to this item: https://ahro.austin.org.au/austinjspui/handle/1/33653
Title: Editorial: Application of radiomics in understanding tumor biological behaviors and treatment response.
Austin Authors: Xiao, Ningping;Pei, Zhengda;Lu, Wenhui;Fang, Rongyao;Jin, Yi;Zhou, Guanzhi;Meng, Xue;Ng, Sweet Ping ;Xing, Lei;Liao, Zhongxing;Sijtsema, Nanna Maria;Yang, Pei
Affiliation: Hunan Cancer Hospital, Xiangya School of Medicine, Central South University, Changsha, Hunan, China.
Graduate Collaborative Training Base of Hunan Cancer Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, China.
Department of Radiotherapy, University Medical Center Groningen, Groningen, Netherlands.
Department of Radiation Oncology, Shandong Cancer Hospital, Jinan, Shandong, China.
Radiation Oncology
School of Medicine, Stanford University, Stanford, CA, United States.
Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Department of Radiotherapy, University Medical Center Groningen, Groningen, Netherlands.
Olivia Newton-John Cancer Wellness and Research Centre
Issue Date: 2023
Date: 2023
Publication information: Frontiers in Oncology 2023; 13
URI: https://ahro.austin.org.au/austinjspui/handle/1/33653
DOI: 10.3389/fonc.2023.1257447
ORCID: 
Journal: Frontiers in Oncology
Start page: 1257447
PubMed URL: 37664068
Type: Journal Article
Subjects: artificial intelligence
biological behaviors
cancer
individual treatment
machine learning
radiomics
Appears in Collections:Journal articles

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