表皮生长因子受体（EGFR）突变状态的精确预测和大体肿瘤体积（GTV）分割是计算机辅助肺腺癌脑转移诊断的关键目标。然而，由于 MR 图像中强度分布不均匀、边界模糊和脑转移 (BM) 形状可变，这两项任务一直面临困难。解决这些挑战的现有方法主要依赖于单任务算法，而忽略了相互依赖性为了全面解决这些挑战，我们提出了一种多任务深度学习模型，可以同时实现 GTV 分割和 EGFR 亚型分类。具体来说，由卷积自注意力模块组成的多尺度自注意力编码器被设计为提取 GTV 分割解码器和 EGFR 基因型分类器的共享空间和全局信息。然后，设计了一个由卷积块和 Transformer 块组成的混合 CNN-Transformer 分类器，以结合全局和局部信息。此外，通过多任务损失函数解决了任务相关性和异质性问题，旨在通过将分割和分类损失函数与可学习权重相结合来平衡上述两个任务。实验结果表明，我们提出的模型取得了优异的性能，超越了单任务学习方法。我们提出的模型在内部测试集上实现了 GTV 分割的平均 Dice 得分 0.89 和 EGFR 基因分型准确度为 0.88，并在 EGFR 基因型预测任务中实现了 0.81 的准确度，在 GTV 分割任务中实现了 0.85 的平均 Dice 得分在外部测试集上。这表明我们提出的方法具有出色的性能和泛化能力。通过引入高效的特征提取模块、混合CNN-Transformer分类器和多任务损失函数，所提出的多任务深度学习网络显着提高了所实现的性能在 GTV 分割和 EGFR 基因分型任务中。因此，该模型可以作为促进临床治疗的非侵入性工具。© 2023。作者。

The precise prediction of epidermal growth factor receptor (EGFR) mutation status and gross tumor volume (GTV) segmentation are crucial goals in computer-aided lung adenocarcinoma brain metastasis diagnosis. However, these two tasks present continuous difficulties due to the nonuniform intensity distributions, ambiguous boundaries, and variable shapes of brain metastasis (BM) in MR images.The existing approaches for tackling these challenges mainly rely on single-task algorithms, which overlook the interdependence between these two tasks.To comprehensively address these challenges, we propose a multi-task deep learning model that simultaneously enables GTV segmentation and EGFR subtype classification. Specifically, a multi-scale self-attention encoder that consists of a convolutional self-attention module is designed to extract the shared spatial and global information for a GTV segmentation decoder and an EGFR genotype classifier. Then, a hybrid CNN-Transformer classifier consisting of a convolutional block and a Transformer block is designed to combine the global and local information. Furthermore, the task correlation and heterogeneity issues are solved with a multi-task loss function, aiming to balance the above two tasks by incorporating segmentation and classification loss functions with learnable weights.The experimental results demonstrate that our proposed model achieves excellent performance, surpassing that of single-task learning approaches. Our proposed model achieves a mean Dice score of 0.89 for GTV segmentation and an EGFR genotyping accuracy of 0.88 on an internal testing set, and attains an accuracy of 0.81 in the EGFR genotype prediction task and an average Dice score of 0.85 in the GTV segmentation task on the external testing set. This shows that our proposed method has outstanding performance and generalization.With the introduction of an efficient feature extraction module, a hybrid CNN-Transformer classifier, and a multi-task loss function, the proposed multi-task deep learning network significantly enhances the performance achieved in both GTV segmentation and EGFR genotyping tasks. Thus, the model can serve as a noninvasive tool for facilitating clinical treatment.© 2023. The Author(s).