Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 Aug 21;15(1):30822.
doi: 10.1038/s41598-025-16362-1.

Artificial intelligence model for predicting early biochemical recurrence of prostate cancer after robotic-assisted radical prostatectomy

Miguel Angel Bergero  1 , Pablo Martínez  2 , Patricio Modina  3 , Ricardo Hosman  2 , Wenceslao Villamil  2 , Romina Gudiño  4 , Carlos David  3 , Lucas Costa  3   4
Affiliations

Artificial intelligence model for predicting early biochemical recurrence of prostate cancer after robotic-assisted radical prostatectomy

Miguel Angel Bergero et al. Sci Rep. .

Abstract

Prostate cancer remains a significant public health concern, with a substantial proportion of patients experiencing biochemical recurrence (BCR) after radical prostatectomy (RP). Traditional risk models, such as CAPRA-S, have demonstrated moderate predictive performance, highlighting the need for more accurate tools. This study aimed to develop a machine learning (ML) model to predict BCR in patients undergoing robot-assisted laparoscopic RP (RALP). A retrospective cohort of 1024 (476 BCR+ and 548 BCR-) patients was analyzed, using a balanced dataset of 25 clinical and pathological variables. Five ML classifiers were evaluated, with XGBoost emerging as the best-performing model, achieving 84% accuracy and an AUC of 0.91. Model validation on an independent dataset of 96 patients confirmed its robustness, with an AUC of 0.89. Decision and calibration curves demonstrated the superior clinical applicability of XGBoost compared to CAPRA-S, indicating improved risk stratification and potential to optimize treatment decisions. The study underscores the value of ML in refining prognosis prediction and guiding therapeutic strategies in prostate cancer. While further validation in diverse clinical settings is necessary, these findings support the integration of ML-based models into clinical decision-making to enhance personalized patient management.

Keywords: Algorithm; Artificial intelligence; Biochemical recurrence; Machine learning; Prostate cancer; Robotic Surgical.

PubMed Disclaimer

Conflict of interest statement

Declarations. Competing interests: The authors declare no competing interests. Informed consent: Patients enrolled in the study voluntarily signed an informed consent form regarding the use of their data in accordance with the principles of the Declaration of Helsinki, complying with the standards and protocols for conducting human research established by the Ethics Committee of the Italian Hospital of Buenos Aires.

Figures

Fig. 1
Fig. 1
Validation of the model. (a) ROC curves illustrating discriminatory performance. (b) Calibration plot showing agreement between predicted and observed risks. (c) Decision-curve analysis comparing net clinical benefit of the XGBoost model with CAPRA-S and default strategies.
Fig. 2
Fig. 2
Variable importance of XGBoost Model.
Fig. 3
Fig. 3
Variable importance based on SHAP values.
See this image and copyright information in PMC

References

    1. Sung, H. et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin.71(3), 209–249. 10.3322/caac.21660 (2021). - PubMed
    1. Hamdy, F. C. et al. 10-year outcomes after monitoring, surgery, or radiotherapy for localized prostate cancer. New Engl. J. Med.375(15), 1415–1424. 10.1056/NEJMoa1606220 (2016). - PubMed
    1. Wilt, T. J. et al. Follow-up of prostatectomy versus observation for early prostate cancer. N. Engl. J. Med.377(2), 132–142. 10.1056/NEJMoa1615869 (2017). - PubMed
    1. Choueiri, T. K. et al. Impact of postoperative prostate-specific antigen disease recurrence and the use of salvage therapy on the risk of death. Cancer116(8), 1887–1892. 10.1002/cncr.25013 (2010). - PubMed
    1. Jackson, W. C. et al. Intermediate endpoints after postprostatectomy radiotherapy: 5-year distant metastasis to predict overall survival. Eur Urol.74(4), 413–419. 10.1016/j.eururo.2017.12.023 (2018). - PubMed

Substances

LinkOut - more resources