在OSCC中开发和验证用于预测瘤进展的机器学习模型
Xueying Mei1,2,3, Wenhao Luo1,2,3, Wan Duan1,2,3
1Hospital of Stomatology, Sun Yat-sen University, Guangzhou, Guangdong, China.
Oral diseases
|October 27, 2024
概括
机器学习模型可以使用患者数据预测口腔状细胞癌 (OSCC) 的进展. 中性粒细胞数量是最重要的预测因素,有助于对OSCC患者进行个性化风险评估.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
背景情况:
- 口腔状细胞癌 (OSCC) 是一个重要的全球健康问题.
- 准确预测OSCC进展对于及时干预和改善患者结果至关重要.
研究的目的:
- 开发和验证机器学习 (ML) 模型,用于预测口腔状细胞癌 (OSCC) 患者的瘤进展.
- 确定预测OSCC进展的关键临床,病理和血液学特征.
- 为OSCC患者提供个人风险估计结果.
主要方法:
- 这是一项从2009年3月至2021年10月期间涉及1163名OSCC患者的回顾性研究.
- 收集临床,病理和血液学数据.
- 探索六个ML算法,使用精度,灵敏度,特异性,F1得分和AUC来评估性能. 用于特征重要性分析的SHAP值.
主要成果:
- 后勤回归模型表现出卓越的性能.
- 在发展队列中,表现最好的模型获得了94.7%的灵敏度和55.3%的特异性 (AUC:0.76 ± 0.09).
- 中性粒细胞数被确定为OSCC进展的最具预测性的特征.
结论:
- ML模型可以显著提高OSCC进展的临床预测.
- 这些使用基本患者信息的预测工具可以提供个性化的风险评估.
- 这些发现可以帮助临床医生指导针对OSCC管理的有针对性的干预措施.
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