研究舌头诊断特征与胃癌之间的关系:基于机器学习的预测模型
Pengfei Tian1, Zonglin Chen2, Biaojie Fang1
1Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China.
概括
这项研究开发了一种使用舌头特征和机器学习进行早期胃癌 (GC) 检测的非侵入性方法. 综合方法在改善诊断准确性和患者治疗结果方面显得有前途.
科学领域:
- 在瘤学瘤学.
- 医学诊断 医学诊断 医学诊断
- 人工智能在医学中的应用
背景情况:
- 胃癌 (GC) 是癌症死亡的主要原因,特别是在中国.
- 目前的早期检测依赖于侵入性方法,如胃镜检查,限制了可访问性.
- 需要非侵入性,准确的诊断工具来早期检测GC.
研究的目的:
- 开发和验证一种用于早期发现胃癌的非侵入性诊断工具.
- 整合传统中医 (TCM) 舌头功能与机器学习 (ML) 算法.
- 提高早期GC诊断的准确性和可访问性.
主要方法:
- 在292名参与者 (146名GC,146名非GC) 的前性,倾向性得分匹配的队列研究中.
- 收集了舌头的特征 (颜色,形态,涂层) 和胃镜检查结果.
- 在多模式数据上训练了七个ML算法,通过LASSO回归进行特征选择和使用交叉验证和独立测试集进行性能评估.
- 协会规则挖掘 (FP-Growth) 探索了预测性舌头胃镜模式.
主要成果:
- GC患者经常出现蓝紫色,裂,胀和刺痛的舌头,灰色黑色的涂层.
- 非GC个体更经常有白色的舌头和剥落的涂层.
- 渐变增强决策树 (GBDT) 模型获得了最高的性能 (测试AUC=0.980,F1=0.932),SHAP分析证实了舌头特征和瘤标志物的预测价值.
结论:
- 与ML集成的基于TCM的舌头诊断为早期GC检测提供了有希望的非侵入性方法.
- 这种方法有可能改善早期诊断率和患者的治疗结果.
- 在更大的队列中进行进一步验证是有必要的,以确定临床效用.
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