scholarly journals Improving the Communication of Benefits and Harms of Treatment Strategies: Decision Aids for Localized Prostate Cancer Treatment Decisions

2012 ◽  
Vol 2012 (45) ◽  
pp. 197-201 ◽  
Author(s):  
R. M. Hoffman
2015 ◽  
Vol 65 (3) ◽  
pp. 239-251 ◽  
Author(s):  
Philippe D. Violette ◽  
Thomas Agoritsas ◽  
Paul Alexander ◽  
Jarno Riikonen ◽  
Henrikki Santti ◽  
...  

2015 ◽  
Vol 193 (4S) ◽  
Author(s):  
Philippe D. Violette ◽  
Thomas Agoritsas ◽  
Jarno Riikonen ◽  
Henrikki Santti ◽  
Paul Alexander ◽  
...  

2019 ◽  
Vol 37 (7) ◽  
pp. 409-429 ◽  
Author(s):  
Ruben D. Vromans ◽  
Mies C. van Eenbergen ◽  
Steffen C. Pauws ◽  
Gijs Geleijnse ◽  
Henk G. van der Poel ◽  
...  

2019 ◽  
Author(s):  
Ruben Danïel Vromans ◽  
Mies van Eenbergen ◽  
Steffen Pauws ◽  
Gijs Geleijnse ◽  
Henk van der Poel ◽  
...  

Decision aids (DAs) have been developed for patients with localized prostate cancer. DAs were reviewed for the International Patient Decision Aid Standards criteria (IPDAS) and various communicative aspects (CAs). Adherence to the IPDAS criteria varied greatly across the DAs. The use of CAs varied substantially by the DAs. CAs such as personalization, interaction, and multimodality can further improve DAs for localized prostate cancer.


Cancers ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 3064
Author(s):  
Jean-Emmanuel Bibault ◽  
Steven Hancock ◽  
Mark K. Buyyounouski ◽  
Hilary Bagshaw ◽  
John T. Leppert ◽  
...  

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.


PLoS ONE ◽  
2015 ◽  
Vol 10 (11) ◽  
pp. e0142812 ◽  
Author(s):  
Yew Kong Lee ◽  
Ping Yein Lee ◽  
Ai Theng Cheong ◽  
Chirk Jenn Ng ◽  
Khatijah Lim Abdullah ◽  
...  

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