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. 2022 Feb;25(2):359-362.
doi: 10.1038/s41391-021-00441-1. Epub 2021 Sep 3.

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

Enrico Checcucci #  1   2   3 , Samanta Rosati #  4 , Sabrina De Cillis  5 , Marica Vagni  4 , Noemi Giordano  4 , Alberto Piana  5 , Stefano Granato  5 , Daniele Amparore  5 , Stefano De Luca  5 , Cristian Fiori  5 , Gabriella Balestra  4 , Francesco Porpiglia  5
Affiliations

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

Enrico Checcucci et al. Prostate Cancer Prostatic Dis. 2022 Feb.

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.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1. Overview of study results.
A Distribution of patients with negative and positive TB for the eight pre-biopsy variable, according to the thresholds/categories used for PPM construction. B Confusion matrix reporting the results of our PPM with respect to the TB outcome. C Distribution of false negative (red) and true positive (green) patients by ISUP. Focusing on the 53 false negative patients, the distribution of ISUP score was: 30.18% (16/53) with ISUP 1, 60.3% (32/53) with ISUP 2, 3.7% (2/53) with ISUP 3, 5.6% (3/53) with ISUP 4, 0% (0/53) with ISUP 5. D ROC curve obtained for the 983 classified patients.
See this image and copyright information in PMC

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. doi: 10.23736/S0393-2249.19.03365-4. - DOI - PubMed
    1. 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. - PubMed
    1. 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. doi: 10.1007/s12553-016-0150-6. - DOI
    1. 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.
    1. Bhandari M, Reddiboina M. Building artificial intelligence-based personalized predictive models. BJU Int. 2019;124:189–91. doi: 10.1111/bju.14746. - DOI - PubMed

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