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Deep learning-based artificial intelligence for prostate cancer detection at biparametric MRI

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Abstract

Purpose

To present fully automated DL-based prostate cancer detection system for prostate MRI.

Methods

MRI scans from two institutions, were used for algorithm training, validation, testing. MRI-visible lesions were contoured by an experienced radiologist. All lesions were biopsied using MRI-TRUS-guidance. Lesions masks, histopathological results were used as ground truth labels to train UNet, AH-Net architectures for prostate cancer lesion detection, segmentation. Algorithm was trained to detect any prostate cancer ≥ ISUP1. Detection sensitivity, positive predictive values, mean number of false positive lesions per patient were used as performance metrics.

Results

525 patients were included for training, validation, testing of the algorithm. Dataset was split into training (n = 368, 70%), validation (n = 79, 15%), test (n = 78, 15%) cohorts. Dice coefficients in training, validation sets were 0.403, 0.307, respectively, for AHNet model compared to 0.372, 0.287, respectively, for UNet model. In validation set, detection sensitivity was 70.9%, PPV was 35.5%, mean number of false positive lesions/patient was 1.41 (range 0–6) for UNet model compared to 74.4% detection sensitivity, 47.8% PPV, mean number of false positive lesions/patient was 0.87 (range 0–5) for AHNet model. In test set, detection sensitivity for UNet was 72.8% compared to 63.0% for AHNet, mean number of false positive lesions/patient was 1.90 (range 0–7), 1.40 (range 0–6) in UNet, AHNet models, respectively.

Conclusion

We developed a DL-based AI approach which predicts prostate cancer lesions at biparametric MRI with reasonable performance metrics. While false positive lesion calls remain as a challenge of AI-assisted detection algorithms, this system can be utilized as an adjunct tool by radiologists.

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Availability of data and material

ProstateX data are publicly available ((https://www.aapm.org/GrandChallenge/PROSTATEx-2/default.asp); NCI dataset is not publicly available.

Code availability

The code is available upon request.

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Funding

This research is funded by intramural research program of NIH.

Author information

Authors and Affiliations

  1. Molecular Imaging Branch, NCI, NIH, Bethesda, MD, USA

    Sherif Mehralivand, Stephanie A. Harmon, Samira Masoudi, Deepak Kesani, Nathan Lay, Peter L. Choyke & Baris Turkbey

  2. NVIDIA Corporation, Santa Clara, CA, USA

    Dong Yang, Daguang Xu, Ziyue Xu & Holger Roth

  3. Laboratory of Pathology, NCI, NIH, Bethesda, MD, USA

    Maria J. Merino

  4. Center for Interventional Oncology, NCI, NIH, Bethesda, MD, USA

    Bradford J. Wood

  5. Department of Radiology, Clinical Center, NIH, Bethesda, MD, USA

    Bradford J. Wood

  6. Urologic Oncology Branch, NCI, NIH, Bethesda, MD, USA

    Peter A. Pinto

  7. Molecular Imaging Branch, National Cancer Institute, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, MD, 20892-1088, USA

    Baris Turkbey

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  1. Sherif Mehralivand
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Corresponding author

Correspondence to Baris Turkbey.

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Conflict of interest

Author BJW is supported by the Intramural Research Program of the NIH and the NIH Center for Interventional Oncology and NIH Grant # Z1A CL040015-08. NIH and Philips/InVivo Inc have a cooperative Research and Development Agreement. NIH and Philips/InVivo Inc have a patent license agreement and NIH and BJW, BT, PAP, PLC may receive royalties. DY, DZ, HR, ZX are NVIDIA Cooperation employees. The remaining authors have no disclosures.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Informed consent was obtained from all individual participants included in the study.

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Mehralivand, S., Yang, D., Harmon, S.A. et al. Deep learning-based artificial intelligence for prostate cancer detection at biparametric MRI. Abdom Radiol 47, 1425–1434 (2022). https://doi.org/10.1007/s00261-022-03419-2

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  • DOI: https://doi.org/10.1007/s00261-022-03419-2

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