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Enhanced PSA Density Prediction Accuracy When Based on Machine Learning

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Abstract

Purpose

The prostate-specific antigen (PSA) density (PSAD) in prostate cancer (PCa) detection has limited applicability and is probably caused by moderate accuracy. The purpose of this study was to create a machine learning (ML) PSAD model that incorporates PSAD predictors for forecasting clinically significant (cs) prostate cancer (PCa) probability and compare its performance to that of the traditional PSAD.

Methods

PSA and prostate volume (PV) were retrieved from the 725 patients that were subjected to prostate biopsy. After resampling and splitting data, we used the training set to create seven ML algorithms. We chose the RF model that was the most accurate. The area under the curve (AUC) accuracy, precision, sensitivity, and specificity of PSAD and RF PSAD diagnostic performance were compared. Additionally, the ML model’s explainability and its website placement were performed.

Results

csPCa was found in 140 males (19.3%). The proposed novel model exhibited much higher evolution metrics than PSAD. AUC for the PSAD and RF PSAD were 0.757 and 0.942, respectively. The reliability diagram indicates that the RF model fits the data well. For the RF model, the decision curve analysis revealed a net benefit of more than 5%, and 40% subjects could avoid unnecessary biopsy. PV was the more important determinant for csPCa. PSA and PV had non-monotonic relationships and a lot of turbulence.

Conclusion

The RF PSAD model demonstrated strong discrimination and clinical value, which could aid urologists in determining whether a prostate biopsy is required.

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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. Clinical Centre “Kragujevac”, Clinic of Urology and Nephrology, Department of Urology, Kragujevac, Serbia

    Miroslav Stojadinovic

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

    Miroslav Stojadinovic

  3. Bioengineering Research and Development Center BioIRC Kragujevac, 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

  6. Department of Urology, Clinic of Urology and Nephrology, Clinical Centre Kragujevac, Zmaj Jovina 30, Kragujevac, 34 000, Serbia

    Miroslav Stojadinovic

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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 [MS] and [BM]. The first draft of the manuscript was written by [MS]. Supervision was done by [SJ]. All authors commented on previous versions of the manuscript. All 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. Enhanced PSA Density Prediction Accuracy When Based on Machine Learning. J. Med. Biol. Eng. 43, 249–257 (2023). https://doi.org/10.1007/s40846-023-00793-0

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  • DOI: https://doi.org/10.1007/s40846-023-00793-0

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