Shao Y, Zhang YX, Chen HH, Lu SS, Zhang SC, Zhang JX. Advances in the application of artificial intelligence in solid tumor imaging. Artif Intell Cancer 2021; 2(2): 12-24 [DOI: 10.35713/aic.v2.i2.12]
Corresponding Author of This Article
Jie-Xin Zhang, MD, PhD, Associate Professor, Senior Researcher, Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, No. 300 Guangzhou Road, Nanjing 210029, Jiangsu Province, China. jiexinzhang@njmu.edu.cn
Research Domain of This Article
Methodology
Article-Type of This Article
Minireviews
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Ying Shao, Department of Laboratory Medicine, People Hospital of Jiangying, Jiangying 214400, Jiangsu Province, China
Yu-Xuan Zhang, Huan-Huan Chen, Shi-Chang Zhang, Jie-Xin Zhang, Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu Province, China
Shan-Shan Lu, Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu Province, China
Author contributions: Shao Y and Zhang YX performed the majority of the writing and they contributed equally to this minireview; Chen HH and Lu SS provided input in writing the paper; Zhang SC and Zhang JX designed the outline and coordinated the writing of the paper.
Supported byThe “The Six Top Talent Project” of Jiangsu Province, No. WSW-004; and National Natural Science Foundation of China, No. 81671836.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Jie-Xin Zhang, MD, PhD, Associate Professor, Senior Researcher, Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, No. 300 Guangzhou Road, Nanjing 210029, Jiangsu Province, China. jiexinzhang@njmu.edu.cn
Received: March 9, 2021 Peer-review started: March 9, 2021 First decision: March 26, 2021 Revised: April 2, 2021 Accepted: April 20, 2021 Article in press: April 20, 2021 Published online: April 28, 2021 Processing time: 48 Days and 4.6 Hours
Abstract
Early diagnosis and timely treatment are crucial in reducing cancer-related mortality. Artificial intelligence (AI) has greatly relieved clinical workloads and changed the current medical workflows. We searched for recent studies, reports and reviews referring to AI and solid tumors; many reviews have summarized AI applications in the diagnosis and treatment of a single tumor type. We herein systematically review the advances of AI application in multiple solid tumors including esophagus, stomach, intestine, breast, thyroid, prostate, lung, liver, cervix, pancreas and kidney with a specific focus on the continual improvement on model performance in imaging practice.
Core Tip: Many reviews have summarized artificial intelligence applications in the diagnosis and treatment of a single tumor type. However, this is the first review to systematically review how artificial intelligence relieves clinical workloads and changes the current medical workflows while maintaining high quality to provide precision medicine in multiple solid tumors. Due to its clear advantage in imaging practice, patients will benefit from early diagnosis and appropriate treatment.
