集成的多中心深度学习系统用于膀癌的预后预测.
Quanhao He1, Bangxin Xiao1, Yiwen Tan2
1Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, P. R. China.
NPJ precision oncology
|October 16, 2024
概括
这项研究引入了一个深度学习系统,用于膀癌 (BCa) 存活风险预测,使用组织学幻灯片. 人工智能模型准确地对患者进行分层,改善个性化治疗和管理,以获得更好的结果.
科学领域:
- 在瘤学瘤学.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 准确的生存风险分层对于个性化膀癌 (BCa) 治疗至关重要.
- 目前的方法可能缺乏最佳患者管理所需的精度.
研究的目的:
- 开发和验证一个端到端的深度学习系统,用于预测BCa患者的整体存活 (OS) 风险,使用组织学幻灯片.
- 评估系统的性能及其改善患者分层的潜力.
主要方法:
- 使用BlaPaSeg分类器生成组织概率热图和细分图.
- 培训了两个预测深度学习网络,即MacroVisionNet和UniVisionNet.
- 探索了6种潜在的BCa预后生物标志物,包括瘤同定位得分 (Coloc) 和综合肌肉瘤得分 (IMTS).
主要成果:
- 布拉帕塞格分类器表现出高精度 (AUC 0.99060.9945).
- 宏观视觉网络和联合视觉网络实现了显著的C指数 (0.6550.853) 并预测高风险组的死亡风险更高 (HR分别为1.975.06和2.134.01).
- 高风险的Coloc和IMTS组显示死亡风险显著增加 (HR 1.4110.16).
结论:
- 开发的深度学习系统有效地预测了膀癌患者的整体生存风险.
- 这种人工智能驱动的方法提高了生存预测的准确性,支持精细的患者管理和个性化治疗策略.
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Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...
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