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  2. 通过深度学习和计算基因病理学,提高皮肤状细胞癌的转移风险预测
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  2. 通过深度学习和计算基因病理学,提高皮肤状细胞癌的转移风险预测

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通过深度学习和计算基因病理学,提高皮肤状细胞癌的转移风险预测

Emilia Peleva1,2, Yue Chen3, Bernhard Finke3

  • 1Centre for Cancer Evolution, Barts Cancer Institute, Queen Mary University of London, London, UK. emilia.peleva@nhs.net.

NPJ precision oncology
|September 2, 2025

在PubMed 上查看摘要

概括
此摘要是机器生成的。

新的深度学习模型cSCCNet使用数字病理学准确预测皮肤状细胞癌 (cSCC) 的转移风险. 这种工具有助于对患者进行分层,并改善这种常见的皮肤癌的预后.

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科学领域:

  • 癌症学
  • 数字病理学
  • 人工智能

背景情况:

  • 皮肤状细胞癌 (cSCC) 是一种具有显著转移潜力的流行性皮肤癌.
  • 在cSCC中转移与预后不佳有关,因此需要改进风险分层方法.

研究的目的:

  • 开发和验证cSCCNet,这是一个用于预测初级cSCC转移风险的深度学习模型.
  • 根据现有的预测工具评估cSCCNet的性能.

主要方法:

  • 通过四个中心的227例初级cSCC病例的数字病理图像开发cSCCNet.
  • 使用20%的延期测试队列进行回顾性分析.
  • 用于模型可解释性的热图生成.

主要成果:

  • 在预测转移风险方面,cSCCNet的AUC值为0. 95和95%.
  • 与基于基因表达的工具和临床病理学分类相比,该模型表现出卓越的性能.
  • 多变量分析证实cSCCNet是转移的独立预测因子.

结论:

  • cSCCNet显示出作为cSCC转移风险预测的可靠和准确工具的潜力.
  • 该模型可以识别超出已确定的风险因素的新型组织病理特征.
  • 将其整合到现有的组织病理学工作流程中,可以改善患者的管理.