论文标题
基于深度学习的方法来揭示多种癌症类型的整个幻灯片图像的肿瘤突变负担状态
Deep learning-based approach to reveal tumor mutational burden status from whole slide images across multiple cancer types
论文作者
论文摘要
肿瘤突变负担（TMB）是免疫疗法的潜在基因组生物标志物。然而，通过整个外显子组测序检测到的TMB缺乏低资源环境中的临床渗透。在这项研究中，我们提出了一个多尺度的深度学习框架，以解决多个癌症TMB预测模型（MC-TMB）的常规使用的整个幻灯片图像的TMB状态的检测。 MC-TMB在交叉验证队列中达到了曲线（AUC）0.818（0.804-0.831）的平均面积，该曲线比每个单尺度模型都表现出较高的性能。通过在X10放大倍率上进行消融测试，也证实了MC-TMB对单肿瘤模型的改进，而高度关注的区域通常对应于致密的淋巴细胞浸润和杂型肿瘤细胞。 MC-TMB算法在外部验证队列上也表现出良好的概括，与其他方法相比，AUC为0.732（0.683-0.761），并且性能更好。总之，我们提出了一种基于深度学习的方法，以揭示从多种癌症类型的常规使用的病理幻灯片中揭示肿瘤突变负担的状态。
Tumor mutational burden (TMB) is a potential genomic biomarker of immunotherapy. However, TMB detected through whole exome sequencing lacks clinical penetration in low-resource settings. In this study, we proposed a multi-scale deep learning framework to address the detection of TMB status from routinely used whole slide images for a multiple cancer TMB prediction model (MC- TMB). The MC-TMB achieved a mean area under the curve (AUC) of 0.818 (0.804-0.831) in the cross-validation cohort, which showed superior performance to each single-scale model. The improvements of MC-TMB over the single-tumor models were also confirmed by the ablation tests on x10 magnification, and the highly concerned regions typically correspond to dense lymphocytic infiltration and heteromorphic tumor cells. MC-TMB algorithm also exhibited good generalization on the external validation cohort with an AUC of 0.732 (0.683-0.761), and better performance when compared to other methods. In conclusion, we proposed a deep learning-based approach to reveal tumor mutational burden status from routinely used pathological slides across multiple cancer types.
