组织病理学图像中的深度学习用于预测肺癌的致癌驱动分子改变:系统回顾和荟萃分析。

IF 3.5 2区 医学 Q2 ONCOLOGY
Translational lung cancer research Pub Date : 2025-05-30 Epub Date: 2025-05-21 DOI:10.21037/tlcr-2024-1196
Rafael Parra-Medina, Gabriela Guerron-Gomez, Daniel Mendivelso-González, Javier Hernan Gil-Gómez, Juan Pablo Alzate, Marcela Gomez-Suarez, Jose Fernando Polo, John Jaime Sprockel, Andres Mosquera-Zamudio
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引用次数: 0

摘要

背景:肺癌(LC)是世界上第二大诊断癌症和癌症死亡率的主要原因。非小细胞肺癌(NSCLC)占85%的病例,以EGFR、ALK、ROS1和KRAS等致癌改变指导靶向治疗。其患病率因种族、吸烟状况和性别而异。人工智能(AI)的进步使苏木精和伊红染色全片图像(H&E WSIs)的分子生物标志物预测成为可能,为精确肿瘤学提供了一种非侵入性方法。本综述评估了深度学习(DL)模型预测H&E wsi在非小细胞肺癌中的致癌驱动因素及其诊断准确性。方法:在Embase、LILACS、Medline、Web of Science和Cochrane中进行一项注册于PROSPERO (CRD42024573602)的系统综述,以确定使用H&E载片进行LC基因改变的DL模型研究。只包括英语和西班牙语研究。提取关键指标进行meta分析。没有lc特异性数据、缺少基本指标或结果不一致的研究被排除在外。结果:我们发现卷积神经网络(cnn)是研究中最常见的架构。此外,在荟萃分析中,ALK{敏感性为84%[95%置信区间(CI): 62-95%]和特异性为85% (95% CI: 55-96%)}, EGFR [80% (95% CI: 72-86%)和特异性为77% (95% CI: 69-83%)]和TP53[敏感性和特异性为70% (95% CI: 65-83%)]是表现出最佳预测能力的致癌驱动分子改变。结论:我们的研究结果强调了这些模型作为筛查工具的潜力,尽管H&E WSI。有必要在不同的人群和临床结果中验证这些预测模型。这种方法至关重要,为精准医学的进步打开了大门,为个性化治疗策略提供了有希望的途径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep learning in histopathology images for prediction of oncogenic driver molecular alterations in lung cancer: a systematic review and meta-analysis.

Background: Lung cancer (LC) is the second most diagnosed cancer and the leading cause of cancer mortality worldwide. Non-small cell lung cancer (NSCLC) accounts for 85% of cases, with oncogenic alterations like EGFR, ALK, ROS1, and KRAS guiding targeted therapies. Their prevalence varies by ethnicity, smoking status, and gender. Advances in artificial intelligence (AI) enable molecular biomarker prediction from hematoxylin and eosin-stained whole-slide images (H&E WSIs), offering a non-invasive approach to precision oncology. This review assesses deep learning (DL) models predicting oncogenic drivers in NSCLC from H&E WSIs and their diagnostic accuracy.

Methods: A systematic review registered in PROSPERO (CRD42024573602) was conducted in Embase, LILACS, Medline, Web of Science, and Cochrane to identify studies on DL models using H&E slides for LC gene alterations. Only English and Spanish studies were included. Key metrics were extracted for meta-analysis. Studies without LC-specific data, missing essential metrics, or with inconsistent results were excluded.

Results: We found evidence that convolutional neural networks (CNNs) were the most common architectures in studies. Also, in the meta-analysis, ALK {sensitivity of 84% [95% confidence interval (CI): 62-95%] and specificity of 85% (95% CI: 55-96%)}, EGFR [80% (95% CI: 72-86%) and specificity of 77% (95% CI: 69-83%)] and TP53 [sensitivity and specificity of 70% (95% CI: 65-83%)] were the oncogenic driver molecular alterations that demonstrated the best predictive capability performance.

Conclusions: Our results emphasize the potential of these models as screening tools despite H&E WSI.It is necessary to validate these predictive models among diverse populations and clinical outcomes. This approach is crucial and leaves an open door for advances in precision medicine, offering promising avenues for personalized treatment strategies.

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来源期刊
CiteScore
7.20
自引率
2.50%
发文量
137
期刊介绍: Translational Lung Cancer Research(TLCR, Transl Lung Cancer Res, Print ISSN 2218-6751; Online ISSN 2226-4477) is an international, peer-reviewed, open-access journal, which was founded in March 2012. TLCR is indexed by PubMed/PubMed Central and the Chemical Abstracts Service (CAS) Databases. It is published quarterly the first year, and published bimonthly since February 2013. It provides practical up-to-date information on prevention, early detection, diagnosis, and treatment of lung cancer. Specific areas of its interest include, but not limited to, multimodality therapy, markers, imaging, tumor biology, pathology, chemoprevention, and technical advances related to lung cancer.
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