通过将血清SERS与可解释的机器学习模型相结合,进行卵巢癌的非侵入性查
Yu Gao1, Wuliji Hasi1, Yanhua Zhang2
1Zhengzhou Research Institute, Harbin Institute of Technology, Zhengzhou, 450000, China; National Key Laboratory of Laser Spatial Information, Harbin Institute of Technology, Harbin, 150080, China.
Talanta
|February 22, 2026
概括
这项研究开发了一种非侵入性的卵巢癌查方法,使用通过表面增强拉曼光谱 (SERS) 和深度学习的血清分析. 组合的LightGBM-DNN模型在检测卵巢癌方面取得了高准确性,提供了一个有前途的诊断策略.
科学领域:
- 生物医学工程 生物医学工程
- 计算生物学 计算生物学
- 在瘤学瘤学.
背景情况:
- 由于当前查方法的局限性,包括成本,可访问性和辐射暴露,对卵巢癌的诊断具有挑战性.
- 早期检测对于治疗决策和改善患者生活质量至关重要.
- 开发高效的,非侵入性查技术是妇科瘤学的重大未满足需求.
研究的目的:
- 开发和验证使用血清成分分析检测卵巢癌的非侵入性诊断模型.
- 将表面增强的拉曼光谱 (SERS) 与先进的机器学习相结合,用于精确的疾病查.
- 为了评估一个结合的光梯度增强机 (LightGBM) 和深度神经网络 (DNN) 模型的性能.
主要方法:
- 从健康个体,卵巢癌患者和卵巢内膜瘤患者的血清样本中收集SERS光谱.
- 采用LightGBM来进行两阶段的特征选择,使用内在重要性得分和SHapley添加式扩展 (SHAP) 值.
- 使用深度神经网络 (DNN) 通过反向传播进行训练,用于分类和性能优化.
主要成果:
- 在五倍交叉验证中,LightGBM特征选择的DNN模型在区分健康的卵巢癌和子宫内膜瘤样本时获得了92.03%的准确性.
- 在一个独立的测试组中,获得了86.96%的准确性.
- 与传统的机器学习算法相比,DNN模型展示了优越的分类性能.
结论:
- 血清SERS与LightGBM-DNN算法的集成为非侵入性卵巢癌查提供了可行的和有前途的战略.
- 这种方法为有效和精确的早期检测提供了潜力,解决了当前诊断方法的局限性.
- 进一步的临床验证可以将这种方法作为妇科癌症诊断中的一个有价值的工具.
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