预测口腔白血病恶性转变:一种多层感知子方法,结合临床病理特征和DNA内容
Guilherme Iani Pontes1, Anna Luíza Damaceno Araújo2, Andresa Borges Soares3
1Institute of Science and Technology, Federal University of São Paulo (ICT-UNIFESP), São José dos Campos, São Paulo, Brazil.
概括
机器学习通过整合DNA内容和组织病理学,准确地预测口腔白血病恶性转变风险. 这有助于对潜在恶性疾病进行个性化的患者管理.
科学领域:
- 在瘤学瘤学.
- 生物技术是生物技术.
- 计算生物学 计算生物学
背景情况:
- 口腔白血病 (OL) 是一种潜在的恶性口腔粘膜疾病.
- 在OL中预测恶性转变 (MT) 是一个重大的临床挑战.
- 目前的方法在风险分层方面缺乏足够的准确性.
研究的目的:
- 开发和评估一种机器学习 (ML) 模型,用于预测 OL 中的 MT 风险.
- 整合基因病理学,人口统计学和DNA内容特征.
- 提高识别高风险OL病例的准确性.
主要方法:
- 97例OL病例 (18MT,79对照) 的回顾性队列研究.
- 通过流细胞计利用临床病理特征和DNA含量 (细胞周期阶段,4cER).
- 训练了一种具有27个特征的多层感知子 (MLP) 模型,采用交叉验证和过量采样.
主要成果:
- 显著的MT预测因素包括4cER,G2阶段,失生症分级和炎症透.
- 优化的ML模型实现了72%的灵敏度,96%的特异性和85.4%的AUC.
- 生存分析表明,模型识别的高风险病例的结果明显较差.
结论:
- 将DNA含量分析与ML集成为分层OL恶性风险提供了一个客观模型.
- 这种方法补充了传统的组织病理学.
- 支持针对潜在恶性疾病的个性化患者管理.
关键词:
DNA 含量 DNA 含量对DNA内容的分析.在OPMDD上工作.皮质表皮质发育不良症机器学习是机器学习.恶性转变的恶性转变口腔癌是口腔癌的一种疾病.在口腔白血病.普洛伊迪 (Ploidy) 是一个平的人.风险预测模型的风险预测模型更多相关视频
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