一种新的恶性结肠直肠多预后诺莫格拉姆的发展
Andrew P Zammit1,2, Yagiz Alp Aksoy3,4,5, Ian Brown1,2,6
1Faculty of Medicine, University of Queensland, Brisbane, Queensland, Australia.
ANZ journal of surgery
|July 5, 2025
概括
这项研究创建了一个机器学习风险计算器,用于预测多角切除术后恶性结肠直肠多的不良结果. 该工具有助于临床决策管理这些息肉,平衡瘤风险与手术风险.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 恶性息肉的最佳治疗包括平衡瘤风险与手术发病率和死亡率.
- 在选择这些病变的最佳管理策略时存在不确定性.
研究的目的:
- 开发一个利用机器学习的在线风险计算器.
- 为了预测恶性结肠直肠多重体的多重切除术后不良结果的风险.
主要方法:
- 一个通用线性混合 (GLM) 模型是使用从2011年到2020年的回顾性数据开发的.
- 机器学习技术应用于全人口恶性多的数据库.
- 不良结局包括残留疾病,淋巴病或延迟复发.
主要成果:
- 最终的GLM模型结合了患者和病理变量,如年龄,性别,息肉位置,入侵深度和瘤等级.
- 该模型实现了曲线下的面积 (AUC) 为0.76.
- 该模型的平均准确度为0.76 (95% CI:0.72-0.80).
结论:
- 开发了一个基于网络的nomogram,以帮助临床决策.
- 该工具有助于确定患者是否需要结直肠切除或可以通过多角切除术进行管理.
- 风险计算器可以在网上获得:https://malignantpolyp.com/risk-calculator.
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