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Improved Prediction of Significant Prostate Cancer Following Repeated Prostate Biopsy by the Random Forest Classifier

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Abstract

Purpose

Numerous strategies and diagnostic tests were proposed in patients suspected of clinically significant (cs) prostate cancer (PCa) after an initial negative prostate biopsy. The study aimed to create a Random Forest (RF) classifier for predicting the probability of csPCa in specimens taken by the repeated systematic prostate biopsy (SBx), and to determine its diagnostic accuracy and clinical utility.

Methods

This retrospective, single-center study included patients who underwent repeated SBx due to clinical suspicion of cancer. Data on patient age, serum prostate-specific antigen (PSA) levels, prostate volume, digital rectal examination, first-degree family history, and histology findings from the SBx were collected for all patients. The area under the curve (AUC), and secondary metrics of clinical prediction models were used to assess their discriminative abilities. Clinical usefulness of final model was tested by the decision curve analysis (DCA). The explainability and website placement of the ML model were also performed.

Results

In total, 204 patients were eligible for analysis. The csPCa was detected in 26% (n = 53) patients. The AUC, accuracy, sensitivity, and specificity for detection of csPCa were 0.94, 0.91, 0.84, and 0.98, respectively. With an optimal threshold of 0.8, about 34% of unnecessary biopsies would be avoided, but correct diagnosis would be delayed in 4.4% csPC cases. PSA level, prostate volume, and age were the top-ranked variables in the RF model.

Conclusion

The RF classifier predicts csPCa with good accuracy and may help urologists when deciding whether the repeated biopsy is necessary to avoid being too invasive.

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Acknowledgements

The authors were financially supported through a research grant N0175014, N175007and III 41007of the Ministry of Science and Technological Development of Serbia and Grants OI174028 from the City of Kragujevac. The authors thank the Ministry for this support.

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Authors and Affiliations

  1. Department of Urology, Clinical Centre “Kragujevac”, Clinic of Urology and Nephrology, ZmajJovina 30, 34 000, Kragujevac, Serbia

    Miroslav Stojadinovic

  2. Faculty of Medical Sciences, University of Kragujevac, Kragujevac, Serbia

    Miroslav Stojadinovic

  3. Bioengineering Research and Development Center BioIRCKragujevac, Kragujevac, Serbia

    Bogdan Milicevic

  4. Faculty of Engineering, University of Kragujevac, Kragujevac, Serbia

    Bogdan Milicevic

  5. Faculty of Medical Sciences, Pharmacology and Toxicology Department, University of Kragujevac, Kragujevac, Serbia

    Slobodan Jankovic

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  1. Miroslav Stojadinovic
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by [Miroslav Stojadinovic] and [Bogdan Milicevic]. The first draft of the manuscript was written by [Miroslav Stojadinovic]. Supervision: [Slobodan Jankovic].All authors commented on the previous versions of the manuscript. All the authors read and approved the final manuscript.

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Correspondence to Miroslav Stojadinovic.

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Stojadinovic, M., Milicevic, B. & Jankovic, S. Improved Prediction of Significant Prostate Cancer Following Repeated Prostate Biopsy by the Random Forest Classifier. J. Med. Biol. Eng. 43, 83–92 (2023). https://doi.org/10.1007/s40846-022-00768-7

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  • DOI: https://doi.org/10.1007/s40846-022-00768-7

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