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 Sep 8;15(9):432.
doi: 10.3390/jpm15090432.

Machine-Learning-Based Survival Prediction in Castration-Resistant Prostate Cancer: A Multi-Model Analysis Using a Comprehensive Clinical Dataset

Jeong Hyun Lee  1 , Jaeyun Jeong  2 , Young Jin Ahn  1 , Kwang Suk Lee  1 , Jong Soo Lee  3 , Seung Hwan Lee  3 , Won Sik Ham  3 , Byung Ha Chung  1 , Kyo Chul Koo  4
Affiliations

Machine-Learning-Based Survival Prediction in Castration-Resistant Prostate Cancer: A Multi-Model Analysis Using a Comprehensive Clinical Dataset

Jeong Hyun Lee et al. J Pers Med. .

Abstract

Purpose: Accurate survival prediction is essential for optimizing the treatment planning in patients with castration-resistant prostate cancer (CRPC). However, the traditional statistical models often underperform due to limited variable inclusion and an inability to account for complex, multidimensional data interactions. Methods: We retrospectively collected 46 clinical, laboratory, and pathological variables from 801 patients with CRPC, covering the disease course from the initial disease diagnosis to CRPC progression. Multiple machine learning (ML) models, including random survival forests (RSFs), XGBoost, LightGBM, and logistic regression, were developed to predict cancer-specific mortality (CSM), overall mortality (OM), and 2- and 3-year survival status. The dataset was split into training and test cohorts (80:20), with 10-fold cross-validation. The performance was assessed using the C-index for regression models and the AUC, accuracy, precision, recall, and F1-score for classification models. Model interpretability was assessed using SHapley Additive exPlanations (SHAP). Results: Over a median follow-up of 24 months, 70.6% of patients experienced CSM. RSFs achieved the highest C-index in the test set for both CSM (0.772) and OM (0.771). For classification tasks, RSFs demonstrated a superior performance in predicting 2-year survival, while XGBoost yielded the highest F1-score for 3-year survival. The SHAP analysis identified time to first-line CRPC treatment and hemoglobin and alkaline phosphatase levels as key predictors of survival outcomes. Conclusion: The RSF and XGBoost ML models demonstrated a superior performance over that of traditional statistical methods in predicting survival in CRPC. These models offer accurate and interpretable prognostic tools that may inform personalized treatment strategies. External validation and the integration of emerging therapies are warranted for broader clinical applicability.

Keywords: castration-resistant; machine learning; prediction algorithms; prostatic neoplasms; survival.

PubMed Disclaimer

Conflict of interest statement

Author Jaeyun Jeong is affiliated with SKTelecom. The company had no role in the design, execution, interpretation, or writing of this study. All other authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Kaplan–Meier curves for cancer-specific survival and overall survival.
Figure 2
Figure 2
SHAP summary plot for XGB regression model based on CSM.
Figure 3
Figure 3
SHAP summary plot for XGB regression model based on OM.
See this image and copyright information in PMC

References

    1. Fujita N., Hatakeyama S., Tabata R., Okita K., Kido K., Hamano I., Tanaka T., Noro D., Tokui N., Suzuki Y., et al. Real-world effects of novel androgen receptor axis-targeted agents on oncological outcomes in non-metastatic castration-resistant prostate cancer: A multi-institutional retrospective study. Prostate Int. 2024;12:46–51. doi: 10.1016/j.prnil.2023.12.002. - DOI - PMC - PubMed
    1. Yuk H.D., Kim M., Keam B., Ku J.H., Kwak C., Jeong C.W. Weekly versus 2-weekly versus 3-weekly docetaxel to treat metastatic castration-resistant prostate cancer. Prostate Int. 2024;12:219–223. doi: 10.1016/j.prnil.2024.09.002. - DOI - PMC - PubMed
    1. Yamada Y., Sakamoto S., Tsujino T., Saito S., Sato K., Nishimura K., Fukushima T., Nakamura K., Yoshikawa Y., Matsunaga T., et al. Clinical significance of primary tumor progression in metastatic hormone-sensitive prostate cancer. Prostate Int. 2025;13:60–66. doi: 10.1016/j.prnil.2024.11.005. - DOI - PMC - PubMed
    1. Kyaw L., Lim Q.Y., Law Y.X.T., Ong C.S.H., Loke W.T., Chiong E., Tiong H.Y. Cardiovascular risks of Asian patients on androgen-receptor-targeted agents for prostate cancer: A systematic review and meta-analysis. Prostate Int. 2024;12:186–194. doi: 10.1016/j.prnil.2024.07.004. - DOI - PMC - PubMed
    1. Pinart M., Kunath F., Lieb V., Tsaur I., Wullich B., Schmidt S., German Prostate Cancer Consortium (DPKK) Prognostic models for predicting overall survival in metastatic castration-resistant prostate cancer: A systematic review. World J. Urol. 2020;38:613–635. doi: 10.1007/s00345-018-2574-2. - DOI - PubMed