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Development and Validation of an Interpretable Artificial Intelligence Model to Predict 10-Year Prostate Cancer
Jean-Emmanuel Bibault1,2, Steven Hancock3, Mark K Buyyounouski3
1Laboratory of Artificial Intelligence in Medicine and Biomedical Physics, Stanford University School of Medicine, Stanford, CA 94304, USA.
Cancers
|July 2, 2021
Summary
A new gradient-boosting model accurately predicts prostate cancer mortality risk within 10 years. This tool aids treatment decisions for patients, especially those with comorbidities, improving prostate cancer care.
Area of Science:
- Oncology
- Medical Informatics
- Machine Learning
Background:
- Prostate cancer treatment relies on risk stratification, which is challenging for patients with comorbidities.
- Accurate prediction of prostate cancer mortality is crucial for guiding treatment strategies.
Purpose of the Study:
- To develop and validate a gradient-boosting model for predicting 10-year prostate cancer mortality risk.
- To provide an interpretable prediction model to aid clinical decision-making.
Main Methods:
- Utilized prospective data from the PLCO Cancer Screening program.
- A dataset of 8776 prostate cancer patients was randomly split into training (n=7021) and testing (n=1755) sets.
- Developed a gradient-boosting model for risk prediction.
Main Results:
- Achieved a prediction accuracy of 0.98 (±0.01).
- Obtained an area under the receiver operating characteristic curve of 0.80 (±0.04).
- The model demonstrated high performance in predicting prostate cancer mortality.
Conclusions:
- The developed gradient-boosting model effectively predicts prostate cancer mortality risk.
- The model supports informed treatment decisions and enhances understanding through AI interpretability.
- This tool is valuable for personalized prostate cancer management.
