基于深度学习预测器的乳腺癌风险是正常组织中衰老细胞的预测器
medRxiv : the preprint server for health sciences
|June 9, 2023
概括
预测乳腺活检的癌症风险现在可以使用深度学习来评估细胞衰老. 该方法改进了当前的风险模型,可以整合到乳腺癌查协议中.
科学领域:
- 在瘤学瘤学.
- 生物医学工程 生物医学工程
- 计算生物学 计算生物学
背景情况:
- 预测来自非恶性活组织的未来癌症风险是一个重大挑战.
- 细胞衰老在癌症中起着复杂的作用,同时起到瘤抑制剂和促进剂的作用.
- 非恶性乳腺活检每年超过100万次,是癌症风险分层的潜在来源.
结论:
- 在非恶性活检中对细胞衰老的基于深度学习的评估能够准确预测未来的癌症风险.
- 使用深度学习的微观图像分析有望改善乳腺癌风险评估.
- 这些模型可以整合到现有的乳腺癌查和风险分层协议中.
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