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首页> 外文会议>IEEE International Conference on Bioinformatics and Biomedicine >Predicting Tumor Mutational Burden from Liver Cancer Pathological Images Using Convolutional Neural Network
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Predicting Tumor Mutational Burden from Liver Cancer Pathological Images Using Convolutional Neural Network

机译：使用卷积神经网络从肝癌病理图像预测肿瘤突变负担

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Tumor mutational burden (TMB) is the most important and most promising biomarker in the era of tumor immunotherapy, and it can predict the immunotherapy efficiency of patients in various cancers including liver cancer. TMB is mainly obtained by next generation sequencing technology such as whole exome sequencing (WES). However, conditions such as excessive testing costs, lengthy detection cycles, and tissue sample dependence severely limit the clinical application of TMB. Inspired by the inner link between the intrinsic characteristics of the tumor cell genome and the pathological features of tumor cells and their microenvironment-related cells, we propose a deep learning method for predicting the level of TMB (high or low) directly from pathological images. This study found that the feature scale (receptive field) is the biggest factor affecting the classification effect of TMB prediction, and further determined the best receptive field through a series of experiments. Experimental results show that our method is far more out performance of the commonly used panel sequencing (99.7% VS 79.2%). To the best of our knowledge, this is the first research to predict TMB and the highest level of accuracy of genomic characteristic predicted by pathological images. The proposed method has the potential to provide immunotherapy to a much broader subset of patients with liver cancer.
机译：肿瘤突变负担（TMB）是肿瘤免疫治疗时代最重要，最有前途的生物标志物，它可以预测患者在包括肝癌在内的各种癌症中的免疫治疗效率。 TMB主要是通过下一代测序技术（例如全外显子组测序（WES））获得的。但是，诸如过高的测试成本，冗长的检测周期和组织样本依赖性等条件严重限制了TMB的临床应用。受肿瘤细胞基因组的内在特性与肿瘤细胞及其微环境相关细胞的病理特征之间的内在联系的启发，我们提出了一种直接从病理图像预测TMB水平（高或低）的深度学习方法。本研究发现特征尺度（感受野）是影响TMB预测分类效果的最大因素，并通过一系列实验进一步确定了最佳感受野。实验结果表明，我们的方法比常用的面板测序的性能要好得多（99.7％VS 79.2％）。据我们所知，这是第一个预测TMB的研究，并且是病理图像预测的基因组特征准确性的最高水平。所提出的方法具有为更广泛的肝癌患者提供免疫治疗的潜力。

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