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Article

Development and Validation of an Interpretable Artificial Intelligence Model to Predict 10-Year Prostate Cancer Mortality

1
Laboratory of Artificial Intelligence in Medicine and Biomedical Physics, Stanford University School of Medicine, Stanford, CA 94304, USA
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Radiation Oncology Department, Hôpital Européen Georges Pompidou, Assistance Publique—Hôpitaux de Paris, 75015 Paris, France
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Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA 94305, USA
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Department of Urology, Stanford University School of Medicine, Stanford, CA 94305, USA
*
Authors to whom correspondence should be addressed.
Academic Editor: Ewan Millar
Cancers 2021, 13(12), 3064; https://doi.org/10.3390/cancers13123064
Received: 24 May 2021 / Revised: 3 June 2021 / Accepted: 17 June 2021 / Published: 19 June 2021
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Cancer Research)
This article presents a gradient-boosted model that can predict 10-year prostate cancer mortality with high accuracy. The model was developed and validated on prospective multicenter data from the PLCO trial. Using XGBoost and Shapley values, it provides interpretability to understand its prediction. It can be used online to provide predictions and support informed decision-making in PCa treatment.
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. View Full-Text
Keywords: prostate cancer; artificial intelligence; machine learning; prediction prostate cancer; artificial intelligence; machine learning; prediction
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MDPI and ACS Style

Bibault, J.-E.; Hancock, S.; Buyyounouski, M.K.; Bagshaw, H.; Leppert, J.T.; Liao, J.C.; Xing, L. Development and Validation of an Interpretable Artificial Intelligence Model to Predict 10-Year Prostate Cancer Mortality. Cancers 2021, 13, 3064. https://doi.org/10.3390/cancers13123064

AMA Style

Bibault J-E, Hancock S, Buyyounouski MK, Bagshaw H, Leppert JT, Liao JC, Xing L. Development and Validation of an Interpretable Artificial Intelligence Model to Predict 10-Year Prostate Cancer Mortality. Cancers. 2021; 13(12):3064. https://doi.org/10.3390/cancers13123064

Chicago/Turabian Style

Bibault, Jean-Emmanuel, Steven Hancock, Mark K. Buyyounouski, Hilary Bagshaw, John T. Leppert, Joseph C. Liao, and Lei Xing. 2021. "Development and Validation of an Interpretable Artificial Intelligence Model to Predict 10-Year Prostate Cancer Mortality" Cancers 13, no. 12: 3064. https://doi.org/10.3390/cancers13123064

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