基于预训练基础模型和迁移学习从组织病理全切片图像预测LUAD中的EGFR突变：一项印度队列研究
Predicting EGFR Mutation in LUAD from Histopathological Whole-Slide Images Using Pretrained Foundation Model and Transfer Learning: An Indian Cohort Study
AI总结：
本研究提出基于ViT病理基础模型和ABMIL的深度学习框架，从H&E全切片图像预测LUAD的EGFR突变，在印度队列和TCGA外部测试集上分别达到0.933和0.965的AUC。
摘要：
肺腺癌（LUAD）是非小细胞肺癌（NSCLC）的一种亚型。携带EGFR基因突变的LUAD约占LUAD病例的46%。携带EGFR突变的患者可使用特定的酪氨酸激酶抑制剂（TKIs）进行治疗。因此，预测EGFR突变状态有助于临床决策。H&E染色的全切片成像（WSI）是癌症分期和亚型分型的常规筛查程序，尤其影响东南亚人群，其突变发生率显著高于白种人（39-64%对7-22%）。近期AI模型的进展在癌症检测和分类方面显示出有前景的结果。在本研究中，我们提出了一种基于视觉变换器（ViT）的病理基础模型和基于注意力的多实例学习（ABMIL）架构的深度学习（DL）框架，用于从H&E WSI预测EGFR突变状态。所开发的流程使用印度队列（170个WSI）的数据进行训练，并在两个独立数据集上评估：内部测试集（来自印度队列的30个WSI）和来自TCGA的外部测试集（86个WSI）。该模型在两个数据集上表现一致，内部和外部测试集的AUC分别为0.933（±0.010）和0.965（±0.015）。该框架可以在小数据集上高效训练，与多项先前研究相比，无论训练领域如何，均实现了优越的性能。当前研究证明了使用常规病理切片准确预测EGFR突变状态的可行性，特别是在资源有限的环境中，利用基础模型和基于注意力的多实例学习。
英文摘要：
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.
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