Frontiers in Oncology (Nov 2024)
PET/CT radiomics and deep learning in the diagnosis of benign and malignant pulmonary nodules: progress and challenges
- Yan Sun,
- Yan Sun,
- Yan Sun,
- Xinyu Ge,
- Xinyu Ge,
- Xinyu Ge,
- Rong Niu,
- Rong Niu,
- Rong Niu,
- Jianxiong Gao,
- Jianxiong Gao,
- Jianxiong Gao,
- Yunmei Shi,
- Yunmei Shi,
- Yunmei Shi,
- Xiaoliang Shao,
- Xiaoliang Shao,
- Xiaoliang Shao,
- Yuetao Wang,
- Yuetao Wang,
- Yuetao Wang,
- Xiaonan Shao,
- Xiaonan Shao,
- Xiaonan Shao
Affiliations
- Yan Sun
- Department of Nuclear Medicine, the Third Affiliated Hospital of Soochow University, Changzhou, China
- Yan Sun
- Institute of Clinical Translation of Nuclear Medicine and Molecular Imaging, Soochow University, Changzhou, China
- Yan Sun
- Department of Nuclear Medicine, Changzhou Clinical Medical Center, Changzhou, China
- Xinyu Ge
- Department of Nuclear Medicine, the Third Affiliated Hospital of Soochow University, Changzhou, China
- Xinyu Ge
- Institute of Clinical Translation of Nuclear Medicine and Molecular Imaging, Soochow University, Changzhou, China
- Xinyu Ge
- Department of Nuclear Medicine, Changzhou Clinical Medical Center, Changzhou, China
- Rong Niu
- Department of Nuclear Medicine, the Third Affiliated Hospital of Soochow University, Changzhou, China
- Rong Niu
- Institute of Clinical Translation of Nuclear Medicine and Molecular Imaging, Soochow University, Changzhou, China
- Rong Niu
- Department of Nuclear Medicine, Changzhou Clinical Medical Center, Changzhou, China
- Jianxiong Gao
- Department of Nuclear Medicine, the Third Affiliated Hospital of Soochow University, Changzhou, China
- Jianxiong Gao
- Institute of Clinical Translation of Nuclear Medicine and Molecular Imaging, Soochow University, Changzhou, China
- Jianxiong Gao
- Department of Nuclear Medicine, Changzhou Clinical Medical Center, Changzhou, China
- Yunmei Shi
- Department of Nuclear Medicine, the Third Affiliated Hospital of Soochow University, Changzhou, China
- Yunmei Shi
- Institute of Clinical Translation of Nuclear Medicine and Molecular Imaging, Soochow University, Changzhou, China
- Yunmei Shi
- Department of Nuclear Medicine, Changzhou Clinical Medical Center, Changzhou, China
- Xiaoliang Shao
- Department of Nuclear Medicine, the Third Affiliated Hospital of Soochow University, Changzhou, China
- Xiaoliang Shao
- Institute of Clinical Translation of Nuclear Medicine and Molecular Imaging, Soochow University, Changzhou, China
- Xiaoliang Shao
- Department of Nuclear Medicine, Changzhou Clinical Medical Center, Changzhou, China
- Yuetao Wang
- Department of Nuclear Medicine, the Third Affiliated Hospital of Soochow University, Changzhou, China
- Yuetao Wang
- Institute of Clinical Translation of Nuclear Medicine and Molecular Imaging, Soochow University, Changzhou, China
- Yuetao Wang
- Department of Nuclear Medicine, Changzhou Clinical Medical Center, Changzhou, China
- Xiaonan Shao
- Department of Nuclear Medicine, the Third Affiliated Hospital of Soochow University, Changzhou, China
- Xiaonan Shao
- Institute of Clinical Translation of Nuclear Medicine and Molecular Imaging, Soochow University, Changzhou, China
- Xiaonan Shao
- Department of Nuclear Medicine, Changzhou Clinical Medical Center, Changzhou, China
- DOI
- https://doi.org/10.3389/fonc.2024.1491762
- Journal volume & issue
-
Vol. 14
Abstract
Lung cancer is currently the leading cause of cancer-related deaths, and early diagnosis and screening can significantly reduce its mortality rate. Since some early-stage lung cancers lack obvious clinical symptoms and only present as pulmonary nodules (PNs) in imaging examinations, accurately determining the benign or malignant nature of PNs is crucial for improving patient survival rates. 18F-FDG PET/CT is important in diagnosing PNs, but its specificity needs improvement. Radiomics can provide information beyond traditional visual assessment, overcoming its limitations by extracting high-throughput quantitative features from medical images. Radiomics features based on 18F-FDG PET/CT and deep learning methods have shown great potential in the noninvasive diagnosis of PNs. This paper reviews the latest advancements in these methods and discusses their contributions to improving diagnostic accuracy and the challenges they face.
Keywords
