精确基因分型表型生长因子受体（EGFR）对肺腺癌的治疗计划至关重要。目前，临床确定EGFR基因分型主要依赖于活检和序列检测，这两种方法侵入性和复杂性较强。最近，计算机断层扫描（CT）影像与深度学习技术的结合取得了一种非侵入式且直接的方式，用于识别EGFR基因表型。然而，还存在许多进一步探索的限制：1）大部分方法仍需要医生对肿瘤边界进行注释，这是耗时且容易产生主观错误的；2）现有方法大部分仅从计算机视觉领域借用，并没有充分利用多层次特征进行最终预测。为了解决这些问题，我们提出了一种Denseformer框架，以直接从三维CT肺部图像中以端到端的方式识别EGFR基因突变状态。具体来说，我们将三维完整肺部CT图像作为神经网络模型的输入，而无需手动标记肺结节。这得到了医学报告的启发，即EGFR基因突变状态不仅与局部肿瘤结节有关，还与整个肺部的微环境有关。此外，我们设计了一种新颖的Denseformer网络，可以充分探索不同层次特征之间的独特信息。Denseformer是一种将卷积神经网络（CNN）和Transformer优点相结合的新颖网络架构。Denseformer从三维CT肺部图像中直接学习，保留了CT图像的空间位置信息。为了进一步提高模型性能，我们设计了一个组合Transformer模块。该模块利用Transformer编码器全局集成了不同层次和层次的信息，并将其作为最终预测的基础。我们在遵义医科大学附属医院收集的肺腺癌数据集上测试了所提出的模型。广泛的实验证明了所提出的方法可以有效地从三维CT图像中提取有意义的特征以进行准确的预测。与其他基于CT图像的深度学习方法相比，Denseformer在使用单一模态的CT图像预测EGFR基因突变状态方面表现最佳。

Accurate genotyping of the epidermal growth factor receptor (EGFR) is critical for the treatment planning of lung adenocarcinoma. Currently, clinical identification of EGFR genotyping highly relies on biopsy and sequence testing which is invasive and complicated. Recent advancements in the integration of computed tomography (CT) imagery with deep learning techniques have yielded a non-invasive and straightforward way for identifying EGFR profiles. However, there are still many limitations for further exploration: 1) most of these methods still require physicians to annotate tumor boundaries, which are time-consuming and prone to subjective errors; 2) most of the existing methods are simply borrowed from computer vision field which does not sufficiently exploit the multi-level features for final prediction. To solve these problems, we propose a Denseformer framework to identify EGFR mutation status in a real end-to-end fashion directly from 3D lung CT images. Specifically, we take the 3D whole-lung CT images as the input of the neural network model without manually labeling the lung nodules. This is inspired by the medical report that the mutational status of EGFR is associated not only with the local tumor nodules but also with the microenvironment surrounded by the whole lung. Besides, we design a novel Denseformer network to fully explore the distinctive information across the different level features. The Denseformer is a novel network architecture that combines the advantages of both convolutional neural network (CNN) and Transformer. Denseformer directly learns from the 3D whole-lung CT images, which preserves the spatial location information in the CT images. To further improve the model performance, we designed a combined Transformer module. This module employs the Transformer Encoder to globally integrate the information of different levels and layers and use them as the basis for the final prediction. The proposed model has been tested on a lung adenocarcinoma dataset collected at the Affiliated Hospital of Zunyi Medical University. Extensive experiments demonstrated the proposed method can effectively extract meaningful features from 3D CT images to make accurate predictions. Compared with other state-of-the-art methods, Denseformer achieves the best performance among current methods using deep learning to predict EGFR mutation status based on a single modality of CT images.