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Machine learning-based prediction model and visual interpretation for prostate cancer.

Gang Chen1, Xuchao Dai1, Mengqi Zhang1

  • 1School of Public Health and Management, Wenzhou Medical University, Wenzhou, 325035, China.

BMC Urology
|October 14, 2023
PubMed
Summary

A new XGBoost model improves prostate cancer (PCa) prediction accuracy using routine clinical data. This model, incorporating serum prostate-specific antigen (PSA) and biochemical markers, offers a valuable tool for PCa diagnosis and screening.

Keywords:
Biochemical parametersMachine learningProstate cancerRisk thresholdShapley values

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Area of Science:

  • Urology
  • Oncology
  • Biomedical Informatics

Background:

  • Prostate cancer (PCa) diagnosis heavily relies on serum prostate-specific antigen (PSA) testing, but its accuracy requires enhancement.
  • Developing advanced PCa prediction models with high clinical utility is crucial for improving patient outcomes.

Purpose of the Study:

  • To develop and validate an XGBoost-based prediction model for prostate cancer using readily available clinical and biochemical parameters.
  • To assess the diagnostic performance of the developed model against traditional PSA markers.

Main Methods:

  • Retrospective analysis of benign prostatic hyperplasia (BPH) and PCa patient data from the Chinese National Clinical Medical Science Data Center.
  • Construction of an XGBoost model incorporating age, BMI, PSA parameters, and serum biochemical markers.
  • Evaluation of model clinical utility using decision analysis curve (DCA) and variable importance analysis via the SHAP framework.

Main Results:

  • The XGBoost model achieved a superior AUC of 0.82, outperforming f/tPSA (0.75), tPSA (0.68), and fPSA (0.61).
  • Free-to-total PSA ratio (f/tPSA) was the most significant predictor, followed by inorganic phosphorus (P), potassium (K), creatine kinase MB isoenzyme (CKMB), LDL-C, and creatinine (Cre).
  • Established PCa risk thresholds for key markers: f/tPSA (0.13), P (1.29 mmol/L), K (4.29 mmol/L), CKMB (11.6 U/L), LDL-C (3.05 mmol/L), and Cre (74.5-99.1 umol/L).

Conclusions:

  • The developed XGBoost model demonstrates high clinical utility and widespread applicability, particularly beneficial for resource-limited settings.
  • The identified risk thresholds for biochemical markers can significantly aid in the clinical diagnosis and screening of prostate cancer.