机器学习预测LEEP后残留和复发的高度CIN
Furui Zhai1, Shanshan Mu1, Yinghui Song1
1Gynecological Clinic, Cangzhou Central Hospital, Cangzhou City, Hebei Province, People's Republic of China.
Cancer management and research
|September 11, 2024
概括
这项研究开发了一种ML模型,用于预测循环电外科切除手术 (LEEP) 后高度宫内皮质瘤 (CIN) 复发. XGBoost模型准确地识别了有风险的患者,从而实现了个性化的后续护理.
科学领域:
- 妇科瘤学 妇科瘤学
- 机器学习在医学中的应用
- 宫癌预防 宫癌预防
背景情况:
- 高度的宫内皮瘤 (CIN) 需要有效的治疗和监测.
- 循环电外科切除手术 (LEEP) 是高度CIN的常见治疗方法.
- 在LEEP后预测残留或复发的高度CIN对于个性化患者管理至关重要.
研究的目的:
- 开发和验证一种机器学习 (ML) 模型,用于预测LEEP后残留或复发高度CIN的风险.
- 为了确定LEEP后高度CIN的关键预测因素.
- 在临床实践中创建一个实时风险评估工具.
主要方法:
- 对532名接受LEEP治疗高度CIN的患者进行了回顾性分析.
- 评估八个ML算法,包括XGBoost,使用AUROC,准确度和灵敏度等指标.
- 使用SHAP值和交叉验证进行特征选择和重要性分析.
主要成果:
- XGBoost ML模型实现了最高的预测性能 (AUROC).
- 确定的关键预测因素包括年龄,ThinPrep细胞检测 (TCT) 结果,HPV分类,CIN严重程度,腺体参与和边缘状态.
- CIN严重程度和保证金状态是最有影响力的预测因素.
结论:
- 对LEEP后高度CIN的基于ML的预测模型在妇科瘤学中取得了重大进展.
- 该模型增强了个性化的患者护理,并支持知情的临床决策.
- 在线预测工具可用于实时风险评估.
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