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Development and Validation of an Interpretable Artificial Intelligence Model to Predict 10-Year Prostate Cancer Mortality.
Pubmed ID
34205398 (View this publication on the PubMed website)
Digital Object Identifier
Cancers (Basel). 2021 Jun 19; Volume 13 (Issue 12)
Bibault JE, Hancock S, Buyyounouski MK, Bagshaw H, Leppert JT, Liao JC, Xing L
  • Laboratory of Artificial Intelligence in Medicine and Biomedical Physics, Stanford University School of Medicine, Stanford, CA 94304, USA.
  • Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA 94305, USA.
  • Department of Urology, Stanford University School of Medicine, Stanford, CA 94305, USA.

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.

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