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External validation of the computerized analysis of TRUS of the prostate with the ANNA/C-TRUS system: a potential role of artificial intelligence for improving prostate cancer detection

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Abstract

Purpose

Prostate cancer (PCa) imaging has been revolutionized by the introduction of multi-parametric Magnetic Resonance Imaging (mpMRI). Transrectal ultrasound (TRUS) has always been considered a low-performance modality. To overcome this, a computerized artificial neural network analysis (ANNA/C–TRUS) of the TRUS based on an artificial intelligence (AI) analysis has been proposed. Our aim was to evaluate the diagnostic performance of the ANNA/C-TRUS system and its ability to improve conventional TRUS in PCa diagnosis.

Methods

We retrospectively analyzed data from 64 patients with PCa and scheduled for radical prostatectomy who underwent TRUS followed by ANNA/C-TRUS analysis before the procedure. The results of ANNA/C-TRUS analysis with whole mount sections from final pathology.

Results

On a per-sectors analysis, sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV) and accuracy were 62%, 81%, 80%, 64% and 78% respectively. The values for the detection of clinically significant prostate cancer were 69%, 77%, 88%, 50% and 75%. The diagnostic values for high grade tumours were 70%, 74%, 91%, 41% and 74%, respectively. Cancer volume (≤ 0.5 or greater) did not influence the diagnostic performance of the ANNA/C-TRUS system.

Conclusions

ANNA/C-TRUS represents a promising diagnostic tool and application of AI for PCa diagnosis. It improves the ability of conventional TRUS to diagnose prostate cancer, preserving its simplicity and availability. Since it is an AI system, it does not hold the inter-observer variability nor a learning curve. Multicenter biopsy-based studies with the inclusion of an adequate number of patients are needed to confirm these results.

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Acknowledgements

The authors thank all the staff of the Oncologic Surgery Division 2 of Institut Paoli-Calmettes Cancer Center, Laetitia Michel, Manon Rinaldo, Sophia Anane and Hadhirami Ibouroi for their help in the everyday practice.

Author information

Authors and Affiliations

  1. Department of Urology, Institut Paoli-Calmettes Cancer Center, Marseille, France

    Vito Lorusso, Geraldine Pignot, Nicolas Branger, Andrea Pacchetti & Jochen Walz

  2. Urology Unit, Fondazione IRCCS Istituto Nazionale Dei Tumori, Milan, Italy

    Vito Lorusso & Nicola Nicolai

  3. University of Milan, Milan, Italy

    Vito Lorusso, Gennaro Musi, Emanuele Montanari & Ottavio de Cobelli

  4. Department of Urology, CHU Yalgado Ouédraogo, Ouagadougou, Burkina Faso

    Boukary Kabre

  5. Department of Pathology, Institut Paoli-Calmettes Cancer Center, Marseille, France

    Jeanne Thomassin-Piana

  6. Department of Radiology, Institut Paoli-Calmettes Cancer Center, Marseille, France

    Serge Brunelle

  7. Department of Urology, IEO, European Institute of Oncology IRCCS, Milan, Italy

    Gennaro Musi & Ottavio de Cobelli

  8. Department of Radiotherapy, Institut Paoli-Calmettes Cancer Center, Marseille, France

    Naji Salem

  9. Department of Urology, Foundation IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy

    Emanuele Montanari

  10. Department of Oncology, Institut Paoli-Calmettes Cancer Center, Marseille, France

    Gwenaelle Gravis

Authors
  1. Vito Lorusso
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  2. Boukary Kabre
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  3. Geraldine Pignot
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  4. Nicolas Branger
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  8. Nicola Nicolai
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  11. Emanuele Montanari
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  13. Gwenaelle Gravis
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  14. Jochen Walz
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Contributions

VL: Protocol, Data collection or management, Data analysis, Manuscript writing. BK: project development, Data analysis. GP: Data collection, Manuscript editing. NB: Data collection, Manuscript editing. AP: Data collection. JTP: Manuscript review. SB: Coordination, Manuscript review. NN: Coordination. GM: Coordination. NS: Coordination, Manuscript editing. EM: Coordination. ODC: Coordination. GG: Manuscript editing. JW: Protocol/project development, Data analysis, Manuscript concept, Coordination.

Corresponding author

Correspondence to Vito Lorusso.

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Conflict of interest

JW declares the following conflicts of interest: Hitachi, Supersonic, ANNA/C-TRUS, 3D-Biopsy, Exact imaging. ANNA/C-TRUS provided image data analysis for research purposes for this study. All other authors declare that they have no conflict of interest.

Ethical approval

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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Lorusso, V., Kabre, B., Pignot, G. et al. External validation of the computerized analysis of TRUS of the prostate with the ANNA/C-TRUS system: a potential role of artificial intelligence for improving prostate cancer detection. World J Urol 41, 619–625 (2023). https://doi.org/10.1007/s00345-022-03965-w

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  • DOI: https://doi.org/10.1007/s00345-022-03965-w

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