Background/Objectives: Lung cancer (NSCLC), which accounts for approximately 85% of lung cancers, exhibits marked heterogeneity that complicates molecular characterization and treatment selection. Recent advances in deep learning (DL) have enabled the extraction of genomic-related morphological features directly from routine Hematoxylin and Eosin (H&E) histopathology, offering a potential complement to Next-Generation Sequencing (NGS) for precision oncology. This review aimed to evaluate how DL models have been applied to predict molecular alterations in NSCLC using H&E-stained slides.Methods: A systematic search following PRISMA 2020 guidelines was conducted across PubMed, Scopus, and Web of Science to identify studies published up to March 2025 that used DL models for mutation prediction in NSCLC. Eligible studies were screened, and data on model architectures, datasets, and performance metrics were extracted.Results: Sixteen studies met inclusion criteria. Most employed convolutional neural networks trained on publicly available datasets such as The Cancer Genome Atlas (TCGA) to infer key mutations including EGFR, KRAS, and TP53. Reported areas under the curve ranged from 0.65 to 0.95, demonstrating variable but promising predictive capability.Conclusions: DL-based histopathology shows strong potential for linking tissue morphology to molecular signatures in NSCLC. However, methodological heterogeneity, small sample sizes, and limited external validation constrain reproducibility and generalizability. Standardized protocols, larger multicenter cohorts, and transparent validation are needed before these models can be translated into clinical practice.
背景/目的：非小细胞肺癌约占肺癌的85%，其显著的异质性使得分子特征分析和治疗选择变得复杂。深度学习的最新进展使得能够直接从常规苏木精-伊红染色组织病理学切片中提取基因组相关的形态学特征，为精准肿瘤学中的下一代测序技术提供了潜在的补充。本综述旨在评估深度学习模型如何应用于基于H&E染色切片预测非小细胞肺癌的分子变异。 方法：遵循PRISMA 2020指南，在PubMed、Scopus和Web of Science数据库中系统检索截至2025年3月发表的利用深度学习模型预测非小细胞肺癌突变的研究。对符合条件的研究进行筛选，并提取模型架构、数据集和性能指标等相关数据。 结果：共有16项研究符合纳入标准。大多数研究采用基于公开数据集（如癌症基因组图谱）训练的卷积神经网络，用于推断包括EGFR、KRAS和TP53在内的关键突变。报道的曲线下面积范围在0.65至0.95之间，显示出虽存在差异但具有前景的预测能力。 结论：基于深度学习的组织病理学在关联非小细胞肺癌组织形态与分子特征方面展现出巨大潜力。然而，方法学异质性、样本量较小以及有限的外部验证限制了结果的可重复性和普适性。在将这些模型转化为临床实践之前，需要建立标准化流程、扩大多中心队列规模并实施透明化验证。
Deep Learning Model-Based Architectures for Lung Tumor Mutation Profiling: A Systematic Review
肿瘤(癌症)患者之家