用图神经网络分析肺癌切片，自动预测空气播散风险
Feature-interactive Siamese graph encoder-based image analysis to predict STAS from histopathology images in lung cancer
- 设计特征交互的孪生图编码器，捕捉组织空间结构
- 内部验证AUC达0.9215，外数据集表现稳定在0.8275以上
- 开源平台支持临床快速诊断，适合病理医生和研究者
肺腺癌中空气播散（STAS）是一种独特的浸润模式，对预后评估和手术决策至关重要。组织病理学是检测STAS的金标准，但传统方法主观性强、耗时长且易误诊，限制了大规模应用。本文提出VERN模型，基于特征交互的孪生图编码器，从肺癌组织病理图像中预测STAS。该模型通过特征共享与跳跃连接捕捉空间拓扑特征，提升训练效果。基于1,546张病理切片构建了单一队列的STAS肺癌数据集。VERN在内部验证中达到AUC 0.9215，冷冻与石蜡包埋样本测试集AUC分别为0.8275和0.8829，表现临床级水平。在单个队列及三个外部数据集上验证均显示强泛化能力，提供开放平台（http://plr.20210706.xyz:5000/），助力提升STAS诊断效率与准确性。
原文摘要 · Abstract (English)
Spread through air spaces (STAS) is a distinct invasion pattern in lung cancer, crucial for prognosis assessment and guiding surgical decisions. Histopathology is the gold standard for STAS detection, yet traditional methods are subjective, time-consuming, and prone to misdiagnosis, limiting large-scale applications. We present VERN, an image analysis model utilizing a feature-interactive Siamese graph encoder to predict STAS from lung cancer histopathological images. VERN captures spatial topological features with feature sharing and skip connections to enhance model training. Using 1,546 histopathology slides, we built a large single-cohort STAS lung cancer dataset. VERN achieved an AUC of 0.9215 in internal validation and AUCs of 0.8275 and 0.8829 in frozen and paraffin-embedded test sections, respectively, demonstrating clinical-grade performance. Validated on a single-cohort and three external datasets, VERN showed robust predictive performance and generalizability, providing an open platform (http://plr.20210706.xyz:5000/) to enhance STAS diagnosis efficiency and accuracy.
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