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The added value of AI-based computer-aided diagnosis in classification of cancer at prostate MRI

  • Imaging Informatics and Artificial Intelligence
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To develop an artificial intelligence (AI) model for prostate segmentation and prostate cancer (PCa) detection, and explore the added value of AI-based computer-aided diagnosis (CAD) compared to conventional PI-RADS assessment.

Methods

A retrospective study was performed on multi-centers and included patients who underwent prostate biopsies and multiparametric MRI. A convolutional-neural-network-based AI model was trained and validated; the reliability of different CAD methods (concurrent read and AI-first read) were tested in an internal/external cohort. The diagnostic performance, consistency and efficiency of radiologists and AI-based CAD were compared.

Results

The training/validation/internal test sets included 650 (400/100/150) cases from one center; the external test included 100 cases (25/25/50) from three centers. For diagnosis accuracy, AI-based CAD methods showed no significant differences and were equivalent to the radiologists in the internal test (127/150 vs. 130/150 vs. 125/150 for reader 1; 127/150 vs.132/150 vs. 131/150 for reader 2; all p > 0.05), whereas in the external test, concurrent-read methods were superior/equal to AI-first read (87/100 vs. 71/100, p < 0.001, for reader 2; 79/100 vs. 69/100, p = 0.076, for reader 1) and better than/equal to radiologists (79/100 vs. 72/100, p = 0.039, for reader 1; 87/100 vs. 86/100, p = 1.000, for reader 2). Moreover, AI-first read/concurrent read improved consistency in both internal test (κ = 1.000, 0.830) and external test (κ = 0.958, 0.713) compared to radiologists (κ = 0.747, 0.600); AI-first read method (8.54 s/7.66 s) was faster than readers (92.72 s/89.54 s) and concurrent-read method (29.15 s/28.92 s), respectively.

Conclusion

AI-based CAD could improve the consistency and efficiency for accurate diagnosis; the concurrent-read method could enhance the diagnostic capabilities of an inexperienced radiologist in unfamiliar situations.

Key Points

• For prostate cancer segmentation, the performance of multi-small Vnet displays optimal compared to small Vnet and Vnet (DSC msvnet vs. DSC svnet , p = 0.021; DSC msvnet vs. DSC vnet , p < 0.001).

• For prostate gland segmentation, the mean/median DSCs for fine and coarse segmentation were 0.91/0.91 and 0.88/0.89, respectively. Fine segmentation displays superior performance compared to coarse (DSC coarse vs. DSC fine , p < 0.001).

• For PCa diagnosis, AI-based CAD methods improve consistency in internal (κ = 1.000; 0.830) and external (κ = 0.958; 0.713) tests compared to radiologists (κ = 0.747; 0.600); the AI-first read (8.54 s/7.66 s) was faster than the readers (92.72 s/89.54 s) and the concurrent-read method (29.15 s/28.92 s).

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Data Availability

The data that support the findings of this study are available from the corresponding author, [Guangyu Wu], upon reasonable request.

Abbreviations

AI:

Artificial intelligence

CAD:

Computer-aided diagnosis

csPCa:

Clinically significant prostate cancer

DSC:

Dice similarity coefficient

FNR:

False-negative rate

FPR:

False-positive rate

GS:

Gleason score

msVnet:

Multi-small Vnet

PCa:

Prostate cancer

PI-RADS:

Prostate Imaging Reporting and Data System

sVnet:

Small Vnet

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Acknowledgements

The authors thank Dr. Yinjie Zhu for his clinical contribution to the study and Dr. Zizhou Zhao, Li Zhu for sharing their expertise.

Funding

This study was supported by the National Natural Science Foundation of China (grant numbers 81601487, 81601453), the Shanghai Jiao Tong University Medical Engineering Cross Fund (grant number YG2021QN27), the Shanghai Municipal Commission of Economy and Informatization (grant number 2019-RGZN-01094), Transverse Project from Renji Hospital, School of Medicine, Shanghai Jiao Tong University (grant number RJKY22-004), the Science and Technology Commission of Shanghai Municipality (grant number 18DZ1930104), and the Shanghai Pujiang Program (grant number 19PJ1431900).

Author information

Author notes

  1. Guiqin Liu and Shixing Pan contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China

    Guiqin Liu, Shihang Pan, Jianrong Xu, Yan Zhou & Guangyu Wu

  2. Department of Radiology, The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China

    Rui Zhao

  3. Department of Radiology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China

    Huang Zhou

  4. Huangpu Branch, Shanghai Ninth People’s Hospital, Shanghai, China

    Jie Chen

  5. Department of Radiology, Putuo Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China

    Xiang Zhou

  6. Department of Urology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China

    Wei Xue

Authors
  1. Guiqin Liu
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Corresponding authors

Correspondence to Wei Xue or Guangyu Wu.

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Guarantor

The scientific guarantor of this publication is Guangyu Wu, PhD.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the institutional review boards.

Ethical approval

This study was approved by the Ethics Committee of Renji Hospital (approval number: KY2018-212); the Ethics Committee of Huangpu Branch, Shanghai Ninth People’s Hospital (approval number: 2020-KYXM-134); the Ethics Committee of Putuo Hospital, Shanghai University of Traditional Chinese Medicine (approval number: 2019FYJ011); and the Ethics Committee of Affiliated Hospital of Nanjing University of Chinese Medicine (approval number: 2019NL-178-22).

Methodology

• retrospective

• diagnostic or prognostic study

• multicenter study

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Liu, G., Pan, S., Zhao, R. et al. The added value of AI-based computer-aided diagnosis in classification of cancer at prostate MRI. Eur Radiol 33, 5118–5130 (2023). https://doi.org/10.1007/s00330-023-09433-2

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