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一个深度学习生成的混合瘤 - 筋膜比率用于预后分层和多omics在膀癌中进行分析
Yifeng He1, Jinbo Xie2, Suquan Zhong3
1Department of Urology, The Affiliated Guangdong Second Provincial General Hospital of Jinan University, Guangzhou 510317, China.
一个深度学习模型从常规幻灯片中准确量化瘤-肌瘤比率 (MTSR),改善膀癌 (BCa) 风险分层. 这种与分子路径相关的MTSR通过MRI放射学可预测,有助于非侵入性精度管理.
科学领域:
- 在瘤学瘤学.
- 计算病理学计算病理学
- 无线电学 (Radiomics) 是一种辐射学.
背景情况:
- 在H&E幻灯片上量化瘤-筋膜结构可以改善膀癌 (BCa) 风险分层.
- 为这种量化开发计算方法对于临床应用至关重要.
研究的目的:
- 开发一种深度学习模型,用于对整个幻灯片图像进行细分,并计算混合瘤-肌瘤比率 (MTSR).
- 在多中心队列中评估MTSR的预后价值.
- 探索MTSR背后的分子机制,并使用多参数MRI (mpMRI) 放射学开发一种非侵入性估计方法.
主要方法:
- 一个ResNet50卷积神经网络被训练在癌症基因组图集 (TCGA) BCa幻灯片上进行细分.
- 计算了MTSR,并使用考克斯回归,卡普兰-梅尔分析和元分析评估了其预后值.
- 进行了多omics (批量和单细胞RNA测序) 和mpMRI放射学分析.
主要成果:
- 美国有线电视新闻网 (CNN) 实现了>90%的细分精度.
- 结合MTSR和N阶段的诺摩图改善了风险预测.
- 高MTSR瘤显示巨细胞透增加和ECM重塑和TGF-β/WNT信号通路的丰富.
- 单细胞分析确定了一种ITGB8高的尿路细胞亚型,具有明显的WNT活性.
- 该mpMRI放射学模型预测了MTSR状态,准确度为~70%.
结论:
- 深度学习衍生的MTSR在BCa中是可复制和预后的.
- 在机理上,MTSR与ITGB8丰富的树皮-瘤性通路有关.
- mpMRI 放射学可用于整合性 BCa 风险分层的非侵入性 MTSR 估计.
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