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PI-RADSAI: introducing a new human-in-the-loop AI model for prostate cancer diagnosis based on MRI

British Journal of Cancer volume 128, pages 1019–1029 (2023)Cite this article

Subjects

Abstract

Background

This study aims to develop and validate an artificial intelligence (AI)-aided Prostate Imaging Reporting and Data System (PI-RADSAI) for prostate cancer (PCa) diagnosis based on MRI.

Methods

The deidentified MRI data of 1540 biopsy-naïve patients were collected from four centres. PI-RADSAI is a two-stage, human-in-the-loop AI capable of emulating the diagnostic acumen of subspecialists for PCa on MRI. The first stage uses a UNet-Seg model to detect and segment biopsy-candidate prostate lesions, whereas the second stage leverages UNet-Seg segmentation is trained specifically with subspecialist’ knowledge-guided 3D-Resnet to achieve an automatic AI-aided diagnosis for PCa.

Results

In the independent test set, UNet-Seg identified 87.2% (628/720) of target lesions, with a Dice score of 44.9% (range, 22.8–60.2%) in segmenting lesion contours. In the ablation experiment, the model trained with the data from three centres was superior (kappa coefficient, 0.716 vs. 0.531) to that trained with single-centre data. In the internal and external tests, the triple-centre PI-RADSAI model achieved an overall agreement of 58.4% (188/322) and 60.1% (92/153) with a referential subspecialist in scoring target lesions; when one-point margin of error was permissible, the agreement rose to 91.3% (294/322) and 97.3% (149/153), respectively. In the paired test, PI-RADSAI outperformed 5/11 (45.5%) and matched the performance of 3/11 (27.3%) general radiologists in achieving a clinically significant PCa diagnosis (area under the curve, internal test, 0.801 vs. 0.770, p < 0.01; external test, 0.833 vs. 0.867, p = 0.309).

Conclusions

Our closed-loop PI-RADSAI outperforms or matches the performance of more than 70% of general readers in the MRI assessment of PCa. This system might provide an alternative to radiologists and offer diagnostic benefits to clinical practice, especially where subspecialist expertise is unavailable.

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Fig. 1: Flowchart of patient enrolment and study design.
Fig. 2: Ablation experiment in the selection of the best PI-RADSAI model.
Fig. 3: Comparison of agreement of PI-RADAI and the general radiologist with a subspecialist for PI-RADS assessment.
Fig. 4: Comparison peformance of AI, general readers and subspecialists for the diagnosis of CsPC.
Fig. 5: Meta-analysis of the performance between AI and individual readers for the diagnosis of CsPC.

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Data availability

Deidentified blinded raw data used to conduct the retrospective and prospective analyses are available upon request to ZY. The source code of the model is archived on GitHub (https://github.com/yuruiqi/PI-RADS_Classification). Requests for the raw images and associated DICOM data used to train and evaluate the model can be directed to Y-DZ, but will only be granted after specific IRB approvals and bespoke data agreement is established between the hospital health network and the requesting party.

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Acknowledgements

We thank all those who helped us during the writing of this research. We also thank the department of Ultrasound, Urology and Pathology of the hospitals for their valuable help and feedback.

Funding

This work is supported by the National Natural Science Foundation of China (contract grant number: 61731009, GY; 82272082, Y-DZ).

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Author notes

  1. These authors contributed equally: Ruiqi Yu, Ke-wen Jiang, Jie Bao.

Authors and Affiliations

  1. Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, 3663N. Zhongshan Rd., 20062, Shanghai, China

    Ruiqi Yu, Yinqiao Yi, Dongmei Wu, Yang Song & Guang Yang

  2. Department of Radiology, the First Affiliated Hospital with Nanjing Medical University, 300N, Guangzhou Rd., 210029, Nanjing, Jiangsu Province, China

    Ke-wen Jiang, Ying Hou & Yu-Dong Zhang

  3. Department of Radiology, the First Affiliated Hospital of Soochow University, 899N, Pinghai Rd., 215006, Suzhou, China

    Jie Bao & Chun-Hong Hu

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Contributions

Study conception: YH, YS, GY and Y-DZ. Data collection: KJ, JB, YH, C-HH and Y-DZ. Data analysis and interpretation: RY, KJ, JB, YH, YY, DW, YS, C-HH, GY and Y-DZ. Technical support: RY, YY, DW, YS, GY and Y-DZ. Administrative support: C-HH, GY and Y-DZ. Manuscript drafting: RY, KJ, GY and Y-DZ. All authors read and approved the final version of the manuscript.

Corresponding authors

Correspondence to Chun-Hong Hu, Guang Yang or Yu-Dong Zhang.

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The authors declare no competing interests.

Ethics approval and consent to participate

Ethics approval for the use of data from centre 1 and centre 2 was granted by the hospital institutional review board (grant no., 2019-SR-396), and informed patient consent was waived.

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Yu, R., Jiang, Kw., Bao, J. et al. PI-RADSAI: introducing a new human-in-the-loop AI model for prostate cancer diagnosis based on MRI. Br J Cancer 128, 1019–1029 (2023). https://doi.org/10.1038/s41416-022-02137-2

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