Radiomics-Based Support Vector Machine Distinguishes Molecular Events Driving the Progression of Lung Adenocarcinoma
Radiomics features can manifest molecular events driving the progression of LUAD. Our study provides molecular insight into radiomics features and assists in the diagnosis and treatment of early-stage LUAD.
Authors
- Meng-Min Wang (ORCID: https://orcid.org/0000-0002-2136-2348)
- Lishan Peng
- Jingyun Ren
- Hui-Zhao Hong
- Zhen‐Bin Qiu (ORCID: https://orcid.org/0000-0003-4106-4011)
- Chao Zhang (ORCID: https://orcid.org/0000-0003-2776-5647)
- Chen Huang (ORCID: https://orcid.org/0000-0002-2407-9716)
- Yi‐Long Wu (ORCID: https://orcid.org/0000-0001-8734-2131)
- Lei Jiang (ORCID: https://orcid.org/0000-0002-5687-8037)
- Hongji Li (ORCID: https://orcid.org/0000-0002-2360-1808)
- Qian Cui (ORCID: https://orcid.org/0000-0001-7973-9649)
- Wen-Zhao Zhong
- Rui Fu (ORCID: https://orcid.org/0000-0001-8893-5924)
- Jia-Tao Zhang
Institutions
- Guangdong Academy of Medical Sciences (CN)
- Guangdong Provincial People's Hospital (CN)
- Southern Medical University (CN)
Publication Details
- Journal
- Journal of Thoracic Oncology
- Published
- 2024-09-19
- DOI
- https://doi.org/10.1016/j.jtho.2024.09.1431
- Citations
- 23
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 9.30
Funders
- National Natural Science Foundation of China
- Guangdong Provincial People's Hospital