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

• Published in: Prostate cancer and prostatic diseases Vol. 25; no. 2; pp. 359 - 362
• Main Authors: Checcucci, Enrico, Rosati, Samanta, De Cillis, Sabrina, Vagni, Marica, Giordano, Noemi, Piana, Alberto, Granato, Stefano, Amparore, Daniele, De Luca, Stefano, Fiori, Cristian, Balestra, Gabriella, Porpiglia, Francesco
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.02.2022; Nature Publishing Group
• Subjects: 692/308/53/2421; 692/699/67/589/466; Algorithms; Artificial Intelligence; Biomedical and Life Sciences; Biomedicine; Biopsy; Brief Communication; Cancer Research; Fuzzy Logic; Humans; Image-Guided Biopsy; Lesions; Magnetic Resonance Imaging; Male; Patients; Prostate - diagnostic imaging; Prostate - pathology; Prostate cancer; Prostate-Specific Antigen; Prostatic Neoplasms - diagnosis; Prostatic Neoplasms - pathology; Reproductive Medicine; Retrospective Studies
• ISSN: 1365-7852; 1476-5608; 1476-5608
• DOI: 10.1038/s41391-021-00441-1

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.