Based on biomedical index data: Risk prediction model for prostate cancer

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

BACKGROUND

Most prostate cancers(PCa) rely on serum prostate-specific antigen (PSA) testing for biopsy confirmation, but the accuracy needs to be further improved. We need to continue to develop PCa prediction model with high clinical application value.

METHODS

Benign prostatic hyperplasia (BPH) and prostate cancer data were obtained from the Chinese National Clinical Medical Science Data Center for retrospective analysis. The model was constructed using the XGBoost algorithm, and patients' age, body mass index (BMI), PSA-related parameters and serum biochemical parameters were used as model variables. Using decision analysis curve (DCA) to evaluate the clinical utility of the models. The shapley additive explanation (SHAP) framework was used to analyze the importance ranking and risk threshold of the variables.

RESULTS

A total of 1915 patients were included in this study, including 823 (43.0%) were BPH patients and 1092 (57.0%) were PCa patients. The XGBoost model provided better performance (AUC 0.82) compared with f/tPSA (AUC 0.75),tPSA (AUC 0.68) and fPSA (AUC 0.61), respectively. Based on SHAP values, f/tPSA was the most important variable, and the top five most important biochemical parameter variables were inorganic phosphorus (P), potassium (K), creatine kinase MB isoenzyme (CKMB), low-density lipoprotein cholesterol (LDL-C), and creatinine (Cre). PCa risk thresholds for these risk markers were f/tPSA (0.13), P (1.29 mmol/L), K (4.29 mmol/L), CKMB ( 11.6U/L), LDL-C (3.05mmol/L) and Cre (74.5-99.1umol/L).

CONCLUSION

The present model has advantages of wide-spread availability and high net benefit, especially for underdeveloped countries and regions. Furthermore, these risk thresholds can assist in the diagnosis and screening of prostate cancer in clinical practice.

Association between Serum Triglycerides and Prostate Specific Antigen (PSA) among U.S. Males: National Health and Nutrition Examination Survey (NHANES), 2003-2010

(1) Background: Increasing evidence indicates that lipid metabolism may influence the concentration of prostate-specific antigen (PSA). However, the association between triglycerides and PSA remains unclear and complicated. Hence, we evaluated the correlation between triglycerides and PSA based on the U.S. National Health and Nutrition Examination Survey (NHANES) database. (2) Methods: A total of 2910 participants out of 41,156 participants fit into our study after conducting the screening from the 2003 to 2010 NHANES survey. Serum triglycerides were the independent variable of our study, and PSA was the dependent variable; (3) Results: In our study, the average age of chosen participants was 59.7 years (±12.7). After adjusting for covariates, the result indicated that for each additional unit of serum triglyceride (mg/dL), the PSA concentrations were reduced by 0.0043 ng/mL (−0.0082, −0.0005) with a statistical difference. Furthermore, we used machine learning of the XGBoost model to determine the relative importance of selected variables as well as constructed a smooth curve based on the fully adjusted model to investigate the possible linear relationship between the triglyceride and PSA concentrations. (4) Conclusions: The serum triglyceride is independently and negatively correlated with PSA among American males, which may make it hard to detect asymptomatic prostate cancer and diagnose at an advance stage with higher triglycerides due to detection bias.