一个深度学习框架,以在整个幻灯片基因病理图像中识别预诊相关的癌症区域 (DOVER)
Xiangxue Wang1, Yufei Zhou2, Cristian Barrera3
1School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, China.
一个新的深度学习框架,DOVER,在全幻灯片图像中识别出预后相关的癌症区域. 这种方法通过精确地确定瘤中的关键区域进行分析,显著改善了癌症结果的预测.
科学领域:
- 计算病理学计算病理学
- 数字病理学数字病理学
- 机器学习在瘤学中
背景情况:
- 全幻灯片图像 (WSIs) 提供了丰富的数据来预测癌症的结果.
- 在异质瘤中确定预后相关区域是一个重大挑战.
- 由于瘤异质性,目前的方法经常与局部预后见解作斗争.
研究的目的:
- 开发一个深度学习框架 (DOVER) 来识别WSIs中的预后相关 (PR) 癌症区域.
- 通过专注于特定的高产区来提高癌症结果预测的准确性.
- 为了弥合局部组织微阵列 (TMA) 数据和大规模WSIs之间的差距.
主要方法:
- DOVER利用已知临床结果的组织微阵列 (TMA) 斑点的模式.
- 从TMA点的预测模式被映射到更大的WSIs以定位PR区域.
- 在这些PR区域上训练深度学习模型以进行特征表示和结果预测.
主要成果:
- 在2041名患者 (NSCLC和OPSCC) 中,DOVER在预后预测的c指数 (p < 0.05) 中取得了超过20%的改善.
- 对DOVER选择区域的分析揭示了免疫细胞 (CD8+,CD20+,CD4+) 和瘤细胞的多样分布,表明了复杂的瘤-免疫相互作用.
- 在DOVER选择的PR区域内发现了统计学上显著的分子和形态差异.
结论:
- DOVER有效地识别了WSIs上的特定空间位置,这些位置对于预测特征提取至关重要.
- 该框架通过专注于预后相关区域来增强临床结果的预测.
- DOVER显示了引导人工智能信息分子分析和个性化癌症治疗策略的潜力.
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