Using Machine Learning to Predict Survival in Patients with Metastatic Castration-Resistant Prostate Cancer.

Journal: Studies in health technology and informatics
PMID: 40200468

Abstract

Non-specific clinical biomarkers have been shown to help identify prognostic risks in cancer patients. However, the accuracy of prognostic biomarkers for predicting survival in patients with metastatic castration-resistant prostate cancer (mCRPC) still has space for improvement. This study aimed to predict 3-year survival in mCRPC patients by analyzing clinical and demographic features. A total of 664 patients with 41 clinical and demographic variables were evaluated. We utilized the class-weighted XGBoost algorithm to address class imbalance and improve the accuracy of outcome predictions. The model achieved an accuracy of 0.73, an AUC of 0.74, a recall, precision and F1 score value of 0.84, indicating a good ability to distinguish between patients who survived less than or more than 3 years. Our findings suggest that PSA, along with other non-specific biomarkers such as albumin and LDH, are significant predictors of survival in mCRPC patients and can be successfully used in machine learning algorithms to predict survival in mCRPC patients.

Authors

  • Xingyue Huo
    University of Utah, Salt Lake City, UT, USA.
  • Manish Kohli
    Lauren C. Harshman and Christopher J. Sweeney, Dana-Farber Cancer Institute, Harvard Medical School; Yu-Hui Chen, Dana-Farber Cancer Institute, Eastern Cooperative Oncology Group-American College of Radiology Imaging Network Cancer Research Group, Boston, MA; Glenn Liu and David Jarrard, University of Wisconsin School of Medicine and Public Health and Carbone Cancer Center, Madison, WI; Michael A. Carducci, Noah Hahn, and Mario Eisenberger, Johns Hopkins University, Baltimore, MD; Robert Dreicer, University of Virginia Cancer Center, Charlottesville, VA; Jorge A. Garcia, Cleveland Clinic Taussig Cancer Institute; Matthew Cooney, University Hospitals Cleveland Medical Center, Seidman Cancer Center, Cleveland, OH; Maha Hussain, Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago; Daniel Shevrin, NorthShore University Health System, Evanston, IL; Manish Kohli, Mayo Clinic, Rochester, MN; Elizabeth R. Plimack, Fox Chase Cancer Center, Temple Health, Philadelphia, PA; Nicholas J. Vogelzang, Comprehensive Cancer Centers of Nevada, Las Vegas, NV; Joel Picus, Siteman Cancer Center, Washington University School of Medicine, St Louis, MO; and Robert Dipaola, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ.
  • Joseph Finkelstein
    Department of Biomedical Informatics, School of Medicine, University of Utah, USA.

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