Bibault Jean-Emmanuel, Hancock Steven, Buyyounouski Mark K, Bagshaw Hilary, Leppert John T, Liao Joseph C, Xing Lei
Laboratory of Artificial Intelligence in Medicine and Biomedical Physics, Stanford University School of Medicine, Stanford, CA 94304, USA.
Radiation Oncology Department, Hôpital Européen Georges Pompidou, Assistance Publique-Hôpitaux de Paris, 75015 Paris, France.
Cancers (Basel). 2021 Jun 19;13(12):3064. doi: 10.3390/cancers13123064.
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 ( = 7021) and testing ( = 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.
前列腺癌的治疗策略以风险分层为指导。在一些患有已知合并症的患者中，这种分层可能会很困难。需要新的模型来指导治疗策略，并确定哪些患者有前列腺癌死亡风险。本文提出了一种梯度提升模型，用于预测癌症诊断后10年内前列腺癌死亡风险，并提供可解释的预测。这项工作使用了来自前列腺、肺癌、结直肠癌和卵巢癌（PLCO）癌症筛查的前瞻性数据，并选取了被诊断为前列腺癌的患者。在随访期间，8776名患者被诊断为前列腺癌。数据集被随机分为训练集（n = 7021）和测试集（n = 1755）。准确率为0.98（±0.01），受试者工作特征曲线下面积为0.80（±0.04）。该模型可用于支持前列腺癌治疗中的明智决策。人工智能的可解释性为用户提供了对预测结果的全新理解。
