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

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

REVIEW | Updated：2025-12-13

- What benefit can be obtained from magnetic resonance imaging diagnosis with artificial intelligence in prostate cancer compared with clinical assessments?
- What benefit can be obtained from magnetic resonance imaging diagnosis with artificial intelligence in prostate cancer compared with clinical assessments?
- MMR 2024年11卷第2期页码：268-286
- Affiliations：
  
  1.School of Engineering Medicine, Beihang University, Beijing 100191, China
  2.School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
  3.CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing 100190, China
  4.University of Chinese Academy of Sciences, Beijing 100080, China
  5.Department of Urology, Peking University Third Hospital, Peking University, Beijing 100191, China
  6.Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology of the People's Republic of China, Beijing 100191, China
  7.Beijing Engineering Research Center of Cardiovascular Wisdom Diagnosis and Treatment, Beijing 100029, China
- Author bio：
  
  *lujian@bjmu.edu.cn,
  jie.tian@ia.ac.cn,
  jgliu@buaa.edu.cn
- Funds：
  
  the Natural Science Foundation of Beijing(Z200027);the National Natural Science Foundation of China(62027901;81930053);the Key-Area Research and Development Program of Guangdong Province(2021B0101420005)
- DOI：10.1186/s40779-023-00464-w
  中图分类号：
- 纸质出版：2024-04
- Accepted：
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What benefit can be obtained from magnetic resonance imaging diagnosis with artificial intelligence in prostate cancer compared with clinical assessments?[J]. MMR, 2024,11(2):268-286.

Cite this article as: Zhao LT, Liu ZY, Xie WF, Shao LZ, Lu J, Tian J, et al. What benefit can be obtained from magnetic resonance imaging diagnosis with artificial intelligence in prostate cancer compared with clinical assessments?. Mil Med Res. 2023;10(1):29.
What benefit can be obtained from magnetic resonance imaging diagnosis with artificial intelligence in prostate cancer compared with clinical assessments?[J]. MMR, 2024,11(2):268-286. DOI： 10.1186/s40779-023-00464-w.

Cite this article as: Zhao LT, Liu ZY, Xie WF, Shao LZ, Lu J, Tian J, et al. What benefit can be obtained from magnetic resonance imaging diagnosis with artificial intelligence in prostate cancer compared with clinical assessments?. Mil Med Res. 2023;10(1):29. DOI： 10.1186/s40779-023-00464-w.

摘要

Abstract

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.

关键词

Keywords

references

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相关作者

Bing-He Xu

Hai-Li Qian

Fei Ma

Yuan-Yi Wang

Tao Yang

Sang Joon Lee

Jihwan Kim

Yong-Bo Xiang

相关机构

Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College

State Key Laboratory of Molecular Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College

National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College

Department of Mechanical Engineering, Pohang University of Science and Technology

Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College

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