Abstract
Simple SummaryThis 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.
Highlights
Each year in the United States, 180,000 patients are diagnosed with prostate cancer, and 26,120 men die from the disease
The role of PSA-based screening in reducing mortality from prostate cancer is still controversial: The PLCO trial did not find any reduction in mortality [5,6,7]
For patients diagnosed with PCa, the ProtecT trial was conducted to compare the effectiveness of active monitoring, radical prostatectomy, and external-beam radiotherapy
Summary
Each year in the United States, 180,000 patients are diagnosed with prostate cancer, and 26,120 men die from the disease. The role of PSA-based screening in reducing mortality from prostate cancer is still controversial: The PLCO trial did not find any reduction in mortality [5,6,7]. For patients diagnosed with PCa, the ProtecT trial was conducted to compare the effectiveness of active monitoring, radical prostatectomy, and external-beam radiotherapy. At 10 years, prostate-cancer-specific survival rates were 98.8% (97.4–99.5), 99% (97.2–99.6), and 99.6% (98.4–99.9) for active monitoring, surgery, and radiotherapy, respectively. In order to assess whether a patient with prostate cancer would benefit from cancer treatment, we created a model to predict the risk of death from prostate cancer 10 years after diagnosis that would take into account the patient’s comorbidities and cancer’s features. We deployed the model in a web interface that can be used to obtain a personalized prediction and explanation in a format that can be readily implemented in a clinical setting
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