影响摩洛哥结直肠癌患者生存率的风险因素:使用可解释机器学习方法进行生存分析
Imad El Badisy1,2,3, Zineb BenBrahim4, Mohamed Khalis5,6,7,8
1Mohammed VI Center for Research and Innovation, Rabat, Morocco. ielbadisy@um6ss.ma.
Scientific reports
|February 12, 2024
概括
这项研究使用机器学习确定了摩洛哥结直肠癌存活率的关键预后因素. 年龄较大,居住在农村,缺乏保险,晚期,没有手术都与较差的结果有关.
科学领域:
- 在瘤学瘤学.
- 机器学习 机器学习
- 公共卫生 公共卫生
背景情况:
- 结肠直肠癌 (CRC) 在全球和摩洛哥构成了重大健康挑战.
- 准确的预后因素识别对于有效的CRC患者管理和治疗策略至关重要.
研究的目的:
- 评估摩洛哥大肠直肠癌患者的3年整体生存率.
- 通过可解释的机器学习方法,确定与结直肠癌存活率相关的强有力的预后因素.
主要方法:
- 在哈桑二世大学医院对343名结肠直肠癌患者进行了回顾性研究.
- 使用非参数生存随机森林 (RSF) 模型进行变量重要性和部分依赖分析.
- 使用协同指数 (C指数) 和屏障评分 (BS) 评估预测性能.
主要成果:
- 1,2,3年的总生存率分别为87%,77%和60%.
- 随机生存森林 (RSF) 确定了手术,阶段,保险,居住和年龄作为关键的预后因素.
- 与考克斯比例危险模型相比,RSF表现出更高的区分能力 (C指数0.798) 和预测准确性 (BS 0.207).
结论:
- 70岁以上的患者,居住在农村地区,缺乏医疗保险,患有远期癌症,并且没有接受过手术,构成了高风险子组.
- 可解释机器学习方法,特别是RSF,为结直肠癌存活率提供了更高的预测准确性.
- 调查结果强调了针对性干预和改善医疗保健的必要性,以改善摩洛哥脆弱的结直肠癌患者群体的医疗保健.
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