Lung cancer is the leading cause of cancer-related death in the United States. Lung adenocarcinoma (LUAD) is one of the most common subtypes of lung cancer that can be treated with resection. While resection can be curative, there is a significant risk of recurrence, which necessitates close monitoring and additional treatment planning. Traditionally, microscopic evaluation of tumor grading in resected specimens is a standard pathologic practice that informs subsequent therapy and patient management. However, this approach is labor-intensive and subject to inter-observer variability. To address the challenge of accurately predicting recurrence, we propose a deep learning-based model to predict the 5-year recurrence of LUAD in patients following surgical resection. In our model, we introduce an innovative dual-attention architecture that significantly enhances computational efficiency. Our model demonstrates excellent performance in recurrent risk stratification, achieving a hazard ratio of 2.29 (95% CI: 1.69–3.09,p< 0.005), which outperforms several existing deep learning methods. This study contributes to ongoing efforts to use deep learning models for automatically learning histologic patterns from whole slide images (WSIs) and predicting LUAD recurrence risk, thereby improving the accuracy and efficiency of treatment decision making.
肺癌是美国癌症相关死亡的主要原因。肺腺癌作为肺癌最常见的亚型之一，可通过手术切除进行治疗。尽管切除术具有治愈潜力，但存在显著的复发风险，因此需要密切监测并制定后续治疗方案。传统上，对切除标本进行肿瘤分级的显微镜评估是指导后续治疗和患者管理的标准病理学实践，但该方法劳动强度大且存在观察者间差异性。为应对准确预测复发的挑战，本研究提出一种基于深度学习的模型，用于预测肺腺癌患者手术切除后的五年复发风险。该模型采用创新的双重注意力架构，显著提升了计算效率。在复发风险分层方面，模型表现出卓越性能，风险比达到2.29（95%置信区间：1.69–3.09，p<0.005），其表现优于现有多种深度学习方法。本研究通过深度学习模型自动学习全切片图像的组织学模式并预测肺腺癌复发风险，有助于提升治疗决策的准确性与效率，为相关领域研究提供了新的技术路径。
Deep Learning Model for Predicting Lung Adenocarcinoma Recurrence from Whole Slide Images
肿瘤(癌症)患者之家