Development and validation of an interpretable machine learning model for predicting Gleason score upgrade in prostate cancer
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View abstract on PubMed
Summary
This summary is machine-generated.Machine learning accurately predicts Gleason score upgrade risk in prostate cancer patients. This tool helps identify high-risk individuals for better treatment decisions.
Area Of Science
- Urology
- Oncology
- Machine Learning
- Medical Informatics
Background
- Gleason score upgrade (GSU) can lead to underestimation of prostate cancer (PCa) aggressiveness.
- This can result in suboptimal treatment decisions for patients.
Purpose Of The Study
- Develop an interpretable machine learning model to predict GSU risk.
- Utilize readily available clinical parameters for prediction.
Main Methods
- Retrospective analysis of radical prostatectomy (RP) patients.
- Development and evaluation of nine machine learning models, including LightGBM.
- Performance assessment using ROC curves, calibration curves, decision curves, and SHAP interpretation.
Main Results
- LightGBM model achieved 84.53% AUC in the test set and 76.61% in external validation.
- Key predictors for GSU include ISUP grade, age, T stage, BMI, PSA, f/t PSA, PLR, and bilateral tumor involvement.
- An online prediction tool was created based on the model.
Conclusions
- An accurate machine learning model and online tool for GSU prediction were developed.
- Identified key factors associated with GSU.
- This approach aids clinicians in identifying high-risk patients for informed treatment planning.
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