与CINTEC®相关的临床和病毒学概况 PLUS积极性:数据驱动的集群和建模研究
Iulian-Valentin Munteanu1, Demetra Socolov2, Razvan Socolov2
1Clinical and Surgical Department, Faculty of Medicine and Pharmacy, 'Dunarea de Jos' University, 800216 Galati, Romania.
Diagnostics (Basel, Switzerland)
|September 13, 2025
概括
患者的年龄和BMI等因素会影响CINtec® PLUS测试结果,用于宫癌查. 机器学习模型可以预测2/3的宫内皮质瘤 (CIN) 风险,帮助管理决策.
科学领域:
- 妇科瘤学 妇科瘤学
- 医学诊断 医学诊断 医学诊断
- 医疗保健中的机器学习
背景情况:
- 对于宫癌查,CINtec® PLUS的诊断准确度可能会受到患者的特征和风险因素的影响.
- 了解这些影响对于准确的解释和患者管理至关重要.
研究的目的:
- 评估和建模与CINtec® PLUS测试阳性相关的风险因素.
- 评估机器学习模型对宫内皮质瘤 (CIN) 的预测性能 2/3.3.
主要方法:
- 对134名感染人类乳头瘤病毒 (HPV) 患者的医疗数据的回顾性分析,这些患者接受了CINtec® PLUS测试.
- 应用梯度增强分类器和XGBoost模型,根据临床风险因素预测CINtec® PLUS阳性和CIN2/3结果.
主要成果:
- 梯度增强模型实现了75%的精度和0.77AUC,用于预测CINtec® PLUS的阳性,身体质量指数和年龄是关键预测指标.
- 高度状内皮损伤 (HSIL),非典型的意义不明的状细胞 (ASC-US) 和高风险的HPV菌株增加了阳性测试的可能性.
- XGBoost模型在CIN2预测 (0.90AUC) 中表现强,但在CIN3预测 (0.58AUC) 中的特异性有限.
结论:
- 患者的特征和风险因素显著影响CINtec® PLUS阳性率.
- 必须仔细考虑这些因素,以制定宫癌查中的适当管理策略.
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