What benefit can be obtained from magnetic resonance imaging diagnosis with artificial intelligence in prostate cancer compared with clinical assessments?

Military Medical Research

Review

Li-Zhi Shao¹  Zhen-Yu Liu²  Jian Lu³  Jie Tian⁴  Jian-Gang Liu⁵  Wan-Fang Xie⁶  Li-Tao Zhao⁶ 

[1] CAS Key Laboratory of Molecular Imaging, Institute of Automation, 100190, Beijing, China;CAS Key Laboratory of Molecular Imaging, Institute of Automation, 100190, Beijing, China;University of Chinese Academy of Sciences, 100080, Beijing, China;Department of Urology, Peking University Third Hospital, Peking University, 100191, Beijing, China;School of Engineering Medicine, Beihang University, 100191, Beijing, China;CAS Key Laboratory of Molecular Imaging, Institute of Automation, 100190, Beijing, China;Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology of the People’s Republic of China, 100191, Beijing, China;School of Engineering Medicine, Beihang University, 100191, Beijing, China;Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology of the People’s Republic of China, 100191, Beijing, China;Beijing Engineering Research Center of Cardiovascular Wisdom Diagnosis and Treatment, 100029, Beijing, China;School of Engineering Medicine, Beihang University, 100191, Beijing, China;School of Biological Science and Medical Engineering, Beihang University, 100191, Beijing, China;

关键词: Clinically significant prostate cancer;    Adverse pathology;    Radiomics quality score;    Artificial intelligence;    Magnetic resonance imaging;   

DOI  :  10.1186/s40779-023-00464-w

 received in 2023-01-16, accepted in 2023-06-07,  发布年份 2023

来源: Springer

【 摘 要 】

The present study aimed to explore the potential of artificial intelligence (AI) methodology based on magnetic resonance (MR) images to aid in the management of prostate cancer (PCa). To this end, we reviewed and summarized the studies comparing the diagnostic and predictive performance for PCa between AI and common clinical assessment methods based on MR images and/or clinical characteristics, thereby investigating whether AI methods are generally superior to common clinical assessment methods for the diagnosis and prediction fields of PCa. First, we found that, in the included studies of the present study, AI methods were generally equal to or better than the clinical assessment methods for the risk assessment of PCa, such as risk stratification of prostate lesions and the prediction of therapeutic outcomes or PCa progression. In particular, for the diagnosis of clinically significant PCa, the AI methods achieved a higher summary receiver operator characteristic curve (SROC-AUC) than that of the clinical assessment methods (0.87 vs. 0.82). For the prediction of adverse pathology, the AI methods also achieved a higher SROC-AUC than that of the clinical assessment methods (0.86 vs. 0.75). Second, as revealed by the radiomics quality score (RQS), the studies included in the present study presented a relatively high total average RQS of 15.2 (11.0–20.0). Further, the scores of the individual RQS elements implied that the AI models in these studies were constructed with relatively perfect and standard radiomics processes, but the exact generalizability and clinical practicality of the AI models should be further validated using higher levels of evidence, such as prospective studies and open-testing datasets.

【 授权许可】

CC BY   
© The Author(s) 2023

【 图 表 】

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