LM3DFN: An end-to-end model for non-invasive prediction of EGFR mutation in non-small cell lung cancer

IF 4.9 2区医学 Q1 ENGINEERING, BIOMEDICAL

Biomedical Signal Processing and Control Pub Date : 2025-10-09 DOI:10.1016/j.bspc.2025.108890

Hui Xie , Yihuai Tang , Hualong She , Qing Li

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引用次数: 0

Abstract

Objective

To explore the feasibility of constructing a deep learning model based on chest CT images to predict epidermal growth factor receptor (EGFR) mutation in non-small cell lung cancer (NSCLC), providing an innovative solution for non-invasive molecular typing.

Methods

This study retrospectively included 623 pathologically confirmed NSCLC patients admitted to our hospital from January 2020 to December 2024 (EGFR mutant: 326 cases, 52.3 %; EGFR non-mutant: 297 cases, 47.7%). All cases had complete CT images and EGFR test results. A Lightweight Multimodal 3D Fusion Network (LM3DFN) deep learning framework was developed, incorporating an attention mechanism to enhance key regional image features and integrate critical imaging information. The dataset was randomly divided into a training set (467 cases) and a test set (156 cases) in a 3:1 ratio. Model performance was evaluated using multi-dimensional metrics, including accuracy (ACC), precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC).

Results

The LM3DFN model demonstrated excellent predictive performance in the test set (ACC = 0.836[0.818–0.854], Precision = 0.825[0.813–0.863], Recall = 0.779[0.751–0.802], F1 = 0.801[0.772–0.834], AUC = 0.889[0.885–0.923]). Visualization of attention analysis indicated a correlation between EGFR mutations and tumor texture and grayscale.

Conclusion

This study confirmed that the LM3DFN model can effectively mine phenotypic features in CT images related to EGFR mutations, providing a non-invasive and reproducible alternative for molecular typing in clinical practice. This model is particularly suitable for dynamic monitoring of gene status evolution during targeted therapy, offering important technical support for the optimization and translational application of precision diagnosis and treatment systems for lung cancer.

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LM3DFN：非小细胞肺癌中EGFR突变的端到端无创预测模型

目的探讨构建基于胸部CT图像的深度学习模型预测非小细胞肺癌（NSCLC）表皮生长因子受体（EGFR）突变的可行性，为非侵入性分子分型提供创新解决方案。方法回顾性分析2020年1月至2024年12月我院收治的623例经病理证实的非小细胞肺癌患者（EGFR突变：326例，52.3%；EGFR非突变：297例，47.7%）。所有病例均有完整的CT图像和EGFR检查结果。开发了轻量级多模态3D融合网络（LM3DFN）深度学习框架，结合注意机制增强关键区域图像特征并整合关键成像信息。数据集随机分为训练集（467例）和测试集（156例），比例为3:1。使用多维指标评估模型性能，包括准确性（ACC）、精密度、召回率、f1评分和接收者工作特征曲线下面积（AUC）。结果LM3DFN模型在检验集中表现出较好的预测效果（ACC = 0.836[0.818-0.854], Precision = 0.825[0.813-0.863], Recall = 0.779[0.751-0.802], F1 = 0.801[0.772-0.834], AUC = 0.889[0.885-0.923]）。注意分析的可视化显示EGFR突变与肿瘤纹理和灰度之间存在相关性。结论本研究证实LM3DFN模型可以有效地挖掘与EGFR突变相关的CT图像表型特征，为临床实践中的分子分型提供了一种无创、可重复的替代方法。该模型特别适用于靶向治疗过程中基因状态演化的动态监测，为肺癌精准诊疗系统的优化和转化应用提供重要的技术支持。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Biomedical Signal Processing and Control 工程技术-工程：生物医学

CiteScore

9.80

自引率

13.70%

发文量

822

审稿时长

4 months

期刊介绍： Biomedical Signal Processing and Control aims to provide a cross-disciplinary international forum for the interchange of information on research in the measurement and analysis of signals and images in clinical medicine and the biological sciences. Emphasis is placed on contributions dealing with the practical, applications-led research on the use of methods and devices in clinical diagnosis, patient monitoring and management. Biomedical Signal Processing and Control reflects the main areas in which these methods are being used and developed at the interface of both engineering and clinical science. The scope of the journal is defined to include relevant review papers, technical notes, short communications and letters. Tutorial papers and special issues will also be published.