新模型预测大肠癌患者生存
创作:Lexi 审核:Lexi 2020年09月14日
  • 开发了一个梯度增强机器模型来预测结直肠癌(CRC)诊断后10年内的死亡风险;
  • 对在一项前列腺癌、肺癌、结直肠癌和卵巢癌筛查(PLCO)试验中被确诊为CRC的患者(n=2359)使用该模型;
  • 对死亡率的中位随访期为16.8年(14.4-18.9),在随访期间,共686例患者(29%)死于CRC;
  • 利用肿瘤本身的特征和其他重要因素,如社会经济和患者生活方式,该模型可为CRC患者的生存提供较为准确的预测。
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Lexi
结直肠癌(CRC)治疗计划的成功在很大程度上取决于预测治疗潜在益处的能力。最新发表在Gut的研究开发并验证了一个使用梯度推进器预测CRC生存的模型。该模型在个体尺度上具有较高的预测性能。
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Gut [IF:19.819]

Development and validation of a model to predict survival in colorectal cancer using a gradient-boosted machine

开发并验证了一个模型,使用梯度推进器预测结直肠癌生存

10.1136/gutjnl-2020-321799

2020-09-04, Article

Abstract & Authors:展开

Abstract:收起
Objective: 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.
Design : 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.
Results : 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.
Conclusions : 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.

First Authors:
Jean-Emmanuel Bibault

Correspondence Authors:
Jean-Emmanuel Bibault

All Authors:
Jean-Emmanuel Bibault,Daniel T Chang,Lei Xing

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