人工智能增强的细胞形态度风险评分提高了皮肤状细胞癌的预后分层
Manuel J Pérez-Baena1,2, Jian-Hua Mao3,4, Jesús Pérez-Losada1,2
1Instituto de Biología Molecular y Celular del Cáncer, Universidad de Salamanca/CSIC, Salamanca, Spain.
Clinical and experimental dermatology
|August 12, 2023
概括
人工智能和机器学习增强皮肤状细胞癌 (cSCC) 风险分层. 一个新的细胞形态度风险评分 (CMRS) 与BWH分期系统相结合,可显著提高高风险患者的预后准确性.
科学领域:
- 皮肤病学 皮肤病学
- 计算病理学计算病理学
- 在瘤学瘤学.
背景情况:
- 准确的风险分层对于治疗皮肤状细胞癌 (cSCC) 患者至关重要.
- 目前的方法依赖于临床和组织病理学因素,这些因素可能无法捕获所有预后信息.
研究的目的:
- 研究人工智能 (AI) 和机器学习 (ML) 在改善cSCC风险分层方面的实用性.
- 确定AI-ML是否可以识别超越传统因素的新生物标志物.
主要方法:
- 追溯分析了104个带有明确利率的征税cSCC.
- 用AI-ML分析H&E染色的幻灯片,以识别细胞形态生物标记物 (CMB).
- 开发一个细胞形态度风险评分 (CMRS) 和与布里格姆和妇女医院 (BWH) 的分期系统进行比较.
主要成果:
- 在CMRS中,无论传统风险因素如何,预后都存在显著差异.
- 将CMRS与BWH分期系统相结合,改善了预后表现,局部复发和结节转移的C指数为0.91.
- 综合方法实现了高精度 (高达89.16%) 和负预测值 (高达96.00%).
结论:
- 细胞形态度风险评分 (CMRS) 帮助超出已确定的特征的cSCC风险分层.
- CMRS和BWH分期系统的组合为高风险cSCC患者提供了出色的预后性能.
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