Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Brief Communication
  • Published:

Artificial intelligence for target prostate biopsy outcomes prediction the potential application of fuzzy logic

Prostate Cancer and Prostatic Diseases volume 25, pages 359–362 (2022)Cite this article

Subjects

Abstract

Background

In current precision prostate cancer (PCa) surgery era the identification of the best patients candidate for prostate biopsy still remains an open issue. The aim of this study was to evaluate if the prostate target biopsy (TB) outcomes could be predicted by using artificial intelligence approach based on a set of clinical pre-biopsy.

Methods

Pre-biopsy characteristics in terms of PSA, PSA density, digital rectal examination (DRE), previous prostate biopsies, number of suspicious lesions at mp-MRI, lesion volume, lesion location, and Pi-Rads score were extracted from our prospectively maintained TB database from March 2014 to December 2019. Our approach is based on Fuzzy logic and associative rules mining, with the aim to predict TB outcomes.

Results

A total of 1448 patients were included. Using the Frequent-Pattern growth algorithm we extracted 875 rules and used to build the fuzzy classifier. 963 subjects were classified whereas for the remaining 484 subjects were not classified since no rules matched with their input variables. Analyzing the classified subjects we obtained a specificity of 59.2% and sensitivity of 90.8% with a negative and the positive predictive values of 81.3% and 76.6%, respectively. In particular, focusing on ISUP ≥ 3 PCa, our model is able to correctly predict the biopsy outcomes in 98.1% of the cases.

Conclusions

In this study we demonstrated that the possibility to look at several pre-biopsy variables simultaneously with artificial intelligence algorithms can improve the prediction of TB outcomes, outclassing the performance of PSA, its derivates and MRI alone.

Access through your institution
Buy or subscribe

This is a preview of subscription content, access via your institution

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Overview of study results.

Similar content being viewed by others

References

  1. Checcucci E, Amparore D, De Luca S, Autorino R, Fiori C, Porpiglia F. Precision prostate cancer surgery: an overview of new technologies and techniques. Minerva Urol Nefrol. 2019;71:487–501.

    Article  Google Scholar 

  2. Checcucci E, Autorino R, Cacciamani GE, Amparore D, De Cillis S, Piana A, et al. Artificial intelligence and neural networks in Urology: current clinical applications. Minerva Urol Nefrol. 2020;72:49–57.

    Article  PubMed  Google Scholar 

  3. Giannini V, Rosati S, Regge D, Balestra G. Specificity improvement of a CAD system for multiparametric MR prostate cancer using texture features and artificial neural networks. Health Technol. 2017;7:71–80.

    Article  Google Scholar 

  4. Rosati S, Balestra G, Giannini V, Mazzetti S, Russo F, Regge D. ChiMerge discretization method: Impact on a computer aided diagnosis system for prostate cancer in MRI. In: Turin 2015 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Proceedings. IEEE; 2015. p. 297–302.

  5. Bhandari M, Reddiboina M. Building artificial intelligence-based personalized predictive models. BJU Int. 2019;124:189–91.

    Article  Google Scholar 

  6. Hung AJ. Can machine-learning algorithms replace conventional statistics? BJU Int. 2019;123:1.

    Article  Google Scholar 

  7. Checcucci E, De Cillis S, Granato S, Chang P, Afyouni AS, Okhunov Z; Uro-technology and SoMe Working Group of the Young Academic Urologists Working Party of the European Association of Urology. Applications of neural networks in urology: a systematic review. Curr Opin Urol. 2020;30:788–807. https://doi.org/10.1097/MOU.0000000000000814.

  8. Rosati S, Agostini V, Balestra G, Knaflitz M. Basographic gait impairment score: a fuzzy classifier based on foot-floor contact parameters. In: 2014 IEEE International Symposium on Medical Measurements and Applications (MeMeA). 2014. p. 1–5.

  9. Barentsz JO, Richenberg J, Clements R, Choyke P, Verma S, Villeirs G, et al. ESUR prostate MR guidelines 2012. Eur Radiol. 2012;22:746–57. https://doi.org/10.1007/s00330-011-2377-y.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Barentsz JO, Weinreb JC, Verma S, Thoeny HC, Tempany CM, Shtern F, et al. Synopsis of the PI-RADS v2 guidelines for multiparametric prostate magnetic resonance imaging and recommendations for use. Eur Urol. 2016;69:41–9. https://doi.org/10.1016/j.eururo.2015.08.038.

    Article  PubMed  Google Scholar 

  11. Han J, Pei J, Yin Y, Mao R. Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Min Knowl Discov. 2004;8:53–87.

    Article  Google Scholar 

  12. Checcucci E, De Cillis S, Piramide F, Amparore D, Kasivisvanathan V, Giganti F, et al. The role of additional standard biopsy in the MRI-targeted biopsy era. Minerva Urol Nefrol. 2020;72:637–9.

    Article  PubMed  Google Scholar 

  13. Mazzone E, Stabile A, Pellegrino F, Basile G, Cignoli D, Cirulli GO, et al. Positive predictive value of prostate imaging reporting and data system version 2 for the detection of clinically significant prostate cancer: a systematic review and meta-analysis. Eur Urol Oncol. 2020;S2588-9311:30212-1. https://doi.org/10.1016/j.euo.2020.12.004.

  14. Mottet N, van den Bergh RCN, Briers E, Van den Broeck T, Cumberbatch MG, De Santis M, et al EAU-EANM-ESTRO-ESUR-SIOG Guidelines on Prostate Cancer-2020 Update. Part 1: Screening, Diagnosis, and Local Treatment with Curative Intent. Eur Urol. 2021;79:243-62. https://doi.org/10.1016/j.eururo.2020.09.042.

  15. Mortezavi A, Palsdottir T, Eklund M, Chellappa V, Murugan SK, Saba K, et al. Head-to-head Comparison of Conventional, and Image- and Biomarker-based Prostate Cancer Risk Calculators. Eur Urol Focus. 2020;S2405-4569:30113-9. https://doi.org/10.1016/j.euf.2020.05.002. Epub ahead of print.

  16. Roffman DA, Hart GR, Leapman MS, Yu JB, Guo FL, Ali I, et al. Development and Validation of a Multiparameterized Artificial Neural Network for Prostate Cancer Risk Prediction and Stratification. JCO Clin Cancer Inform. 2018;2:1–10. https://doi.org/10.1200/CCI.17.00119.

  17. Amparore D, Campi R, Checcucci E, Sessa F, Pecoraro A, Minervini A, et al. Forecasting the Future of Urology Practice: A Comprehensive Review of the Recommendations by International and European Associations on Priority Procedures During the COVID-19 Pandemic. Eur Urol Focus. 2020;6:1032–48. https://doi.org/10.1016/j.euf.2020.05.007.

  18. Wallis CJD, Novara G, Marandino L, A Bex, AM Kamat, RJ Karnes, et al. Risks from deferring treatment for genitourinary cancers: a collaborative review to aid triage and management during the COVID-19 pandemic. Eur Urol 2020;78:29–42. https://doi.org/10.1016/j.eururo.2020.04.063.

Download references

Author information

Author notes

  1. These authors contributed equally: Enrico Checcucci, Samanta Rosati

  2. These authors jointly supervised this work: Gabriella Balestra, Francesco Porpiglia.

Authors and Affiliations

  1. Department of Surgery, Candiolo Cancer Institute, FPO-IRCCS, Turin, Candiolo, Italy

    Enrico Checcucci

  2. Uro-technology Working Group of the Young Academic Urologists (YAU) Working Party of the European Association of Urology (EAU), Arnhem, The Netherlands

    Enrico Checcucci

  3. Department of Oncology, Division of Urology, University of Turin, San Luigi Gonzaga Hospital, Orbassano, Turin, Italy

    Enrico Checcucci, Sabrina De Cillis, Alberto Piana, Stefano Granato, Daniele Amparore, Stefano De Luca, Cristian Fiori & Francesco Porpiglia

  4. Department of Electronics and Telecommunications, Politecnico di Torino, Torino, Italy

    Samanta Rosati, Marica Vagni, Noemi Giordano & Gabriella Balestra

Authors
  1. Enrico Checcucci
    View author publications

    Search author on:PubMed Google Scholar

  2. Samanta Rosati
    View author publications

    Search author on:PubMed Google Scholar

  3. Sabrina De Cillis
    View author publications

    Search author on:PubMed Google Scholar

  4. Marica Vagni
    View author publications

    Search author on:PubMed Google Scholar

  5. Noemi Giordano
    View author publications

    Search author on:PubMed Google Scholar

  6. Alberto Piana
    View author publications

    Search author on:PubMed Google Scholar

  7. Stefano Granato
    View author publications

    Search author on:PubMed Google Scholar

  8. Daniele Amparore
    View author publications

    Search author on:PubMed Google Scholar

  9. Stefano De Luca
    View author publications

    Search author on:PubMed Google Scholar

  10. Cristian Fiori
    View author publications

    Search author on:PubMed Google Scholar

  11. Gabriella Balestra
    View author publications

    Search author on:PubMed Google Scholar

  12. Francesco Porpiglia
    View author publications

    Search author on:PubMed Google Scholar

Contributions

EC: study design, paper writing, data collection, and data interpretation. SR: data analysis, paper writing. SDC: data collection. MV: data analysis. NG: data analysis. AP: data collection. SG: data collection. DA: study design, supervision. SDL: study design, supervision. CF: study design, supervision. GB: study design, supervision, and data analysis. FP: study design, supervision.

Corresponding author

Correspondence to Enrico Checcucci.

Ethics declarations

Competing interests

The authors declare no competing interests.

Ethics approval

The study was conducted in accordance with good clinical practice guidelines, and informed consent was obtained from the patients. According to Italian law (Agenzia Italiana del Farmaco Guidelines for Observational Studies, March 20, 2008), no formal institutional review board or ethics committee approval was required.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Checcucci, E., Rosati, S., De Cillis, S. et al. Artificial intelligence for target prostate biopsy outcomes prediction the potential application of fuzzy logic. Prostate Cancer Prostatic Dis 25, 359–362 (2022). https://doi.org/10.1038/s41391-021-00441-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41391-021-00441-1

This article is cited by

Search

Advanced search

Quick links