Abstract
Lung adenocarcinoma (LUAD) is a subtype of non-small cell lung cancer (NSCLC). LUAD with mutation in the EGFR gene accounts for approximately 46% of LUAD cases. Patients carrying EGFR mutations can be treated with specific tyrosine kinase inhibitors (TKIs). Hence, predicting EGFR mutation status can help in clinical decision making. H&E-stained whole slide imaging (WSI) is a routinely performed screening procedure for cancer staging and subtyping, especially affecting the Southeast Asian populations with significantly higher incidence of the mutation when compared to Caucasians (39-64% vs 7-22%). Recent progress in AI models has shown promising results in cancer detection and classification. In this study, we propose a deep learning (DL) framework built on vision transformers (ViT) based pathology foundation model and attention-based multiple instance learning (ABMIL) architecture to predict EGFR mutation status from H&E WSI. The developed pipeline was trained using data from an Indian cohort (170 WSI) and evaluated across two independent datasets: Internal test (30 WSI from Indian cohort) set, and an external test set from TCGA (86 WSI). The model shows consistent performance across both datasets, with AUCs of 0.933 (+/-0.010), and 0.965 (+/-0.015) for the internal and external test sets respectively. This proposed framework can be efficiently trained on small datasets, achieving superior performance as compared to several prior studies irrespective of training domain. The current study demonstrates the feasibility of accurately predicting EGFR mutation status using routine pathology slides, particularly in resource-limited settings using foundation models and attention-based multiple instance learning.
Abstract (translated)
肺腺癌（LUAD）是非小细胞肺癌（NSCLC）的一种亚型。在 LUAD 中，EGFR 基因突变约占 46% 的病例。携带 EGFR 突变的患者可以接受特定酪氨酸激酶抑制剂（TKIs）治疗。因此，预测 EGFR 突变状态有助于临床决策制定。HE 染色全切片成像 (WSI) 是癌症分期和分型的一种常规筛查方法，在东南亚人群中尤为适用，因为该地区的突变发病率显著高于高加索人种（39-64% 对比 7-22%）。近期人工智能模型的进步在癌症检测和分类方面取得了令人鼓舞的结果。在这项研究中，我们提出了一种基于视觉变压器 (ViT) 的病理学基础模型和注意力机制的多重实例学习 (ABMIL) 架构构建的深度学习（DL）框架，旨在从 HE WSI 中预测 EGFR 突变状态。该开发流程利用了来自印度队列的数据进行训练（170 张 WSI），并在两个独立数据集上进行了评估：内部测试集（30 张 WSI 来自印度队列）和外部测试集（TCGA 数据集中的 86 张 WSI）。该模型在两个数据集中表现出一致的性能，内部测试集和外部测试集分别获得了 AUC 分数为 0.933 (+/-0.010) 和 0.965 (+/-0.015)。此框架能够在较小的数据集上高效训练，并且无论训练领域如何，其表现均优于先前的多项研究。本研究表明，在资源有限的情况下使用基础模型和注意力机制多重实例学习可以准确预测 EGFR 突变状态，尤其适用于常规病理学切片的应用。
URL
https://arxiv.org/abs/2508.01352
