Development and Validation of an Interpretable Artificial Intelligence Model to Predict 10-Year Prostate Cancer Mortality.
Jean Emmanuel BibaultSteven HancockMark K BuyyounouskiHilary BagshawJohn T LeppertJoseph C LiaoLei XingPublished in: Cancers (2021)
Prostate cancer treatment strategies are guided by risk-stratification. This stratification can be difficult in some patients with known comorbidities. New models are needed to guide strategies and determine which patients are at risk of prostate cancer mortality. This article presents a gradient-boosting model to predict the risk of prostate cancer mortality within 10 years after a cancer diagnosis, and to provide an interpretable prediction. This work uses prospective data from the PLCO Cancer Screening and selected patients who were diagnosed with prostate cancer. During follow-up, 8776 patients were diagnosed with prostate cancer. The dataset was randomly split into a training (n = 7021) and testing (n = 1755) dataset. Accuracy was 0.98 (±0.01), and the area under the receiver operating characteristic was 0.80 (±0.04). This model can be used to support informed decision-making in prostate cancer treatment. AI interpretability provides a novel understanding of the predictions to the users.
Keyphrases
- prostate cancer
- radical prostatectomy
- artificial intelligence
- end stage renal disease
- ejection fraction
- chronic kidney disease
- newly diagnosed
- big data
- papillary thyroid
- machine learning
- cardiovascular events
- peritoneal dialysis
- prognostic factors
- risk factors
- squamous cell carcinoma
- type diabetes
- deep learning
- cardiovascular disease
- young adults
- patient reported