Radiation Oncology, Stanford Medicine, Stanford, California, USA
Radiation Oncology, Stanford Medicine, Stanford, California, USA.
Gut. 2021 May;70(5):884-889. doi: 10.1136/gutjnl-2020-321799. Epub 2020 Sep 4.
The success of treatment planning relies critically on our ability to predict the potential benefit of a therapy. In colorectal cancer (CRC), several nomograms are available to predict different outcomes based on the use of tumour specific features. Our objective is to provide an accurate and explainable prediction of the risk to die within 10 years after CRC diagnosis, by incorporating the tumour features and the patient medical and demographic information.
In the prostate, lung, colorectal and ovarian cancer screening (PLCO) Trial, participants (n=154 900) were randomised to screening with flexible sigmoidoscopy, with a repeat screening at 3 or 5 years, or to usual care. We selected patients who were diagnosed with CRC during the follow-up to train a gradient-boosted model to predict the risk to die within 10 years after CRC diagnosis. Using Shapley values, we determined the 20 most relevant features and provided explanation to prediction.
During the follow-up, 2359 patients were diagnosed with CRC. Median follow-up was 16.8 years (14.4-18.9) for mortality. In total, 686 patients (29%) died from CRC during the follow-up. The dataset was randomly split into a training (n=1887) and a testing (n=472) dataset. The area under the receiver operating characteristic was 0.84 (±0.04) and accuracy was 0.83 (±0.04) with a 0.5 classification threshold. The model is available online for research use.
We trained and validated a model with prospective data from a large multicentre cohort of patients. The model has high predictive performances at the individual scale. It could be used to discuss treatment strategies.
治疗计划的成功与否关键取决于我们预测治疗潜在获益的能力。在结直肠癌（CRC）中，有几种列线图可根据肿瘤特异性特征预测不同的结局。我们的目标是通过纳入肿瘤特征以及患者的医疗和人口统计学信息，对 CRC 诊断后 10 年内死亡的风险进行准确且可解释的预测。
在前列腺、肺、结直肠和卵巢癌筛查（PLCO）试验中，参与者（n=154900）被随机分配接受软性乙状结肠镜筛查，可选择 3 年或 5 年重复筛查，或接受常规护理。我们选择了在随访期间被诊断为 CRC 的患者，以训练一个梯度提升模型来预测 CRC 诊断后 10 年内死亡的风险。使用 Shapley 值，我们确定了 20 个最相关的特征，并提供了预测的解释。
在随访期间，有 2359 名患者被诊断为 CRC。中位随访时间为 16.8 年（14.4-18.9），随访期间总共有 686 名患者（29%）死于 CRC。数据集随机分为训练集（n=1887）和测试集（n=472）。接收器操作特征曲线下面积为 0.84（±0.04），准确度为 0.83（±0.04），分类阈值为 0.5。该模型可在线供研究使用。
我们使用来自大型多中心患者队列的前瞻性数据训练和验证了一个模型。该模型在个体层面上具有较高的预测性能。它可以用于讨论治疗策略。
