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<p>Lung cancer remains a leading cause of global cancer mortality, demanding improved early detection and personalized therapies. This study introduces an innovative framework integrating deep learning (DL), radiomics, and explainable AI (XAI) to significantly advance lung nodule analysis and predict actionable genetic mutations. We synergize advanced neural networks (U-Net, DeepLabV3 for segmentation; YOLOv7 for detection) with comprehensive radiomic features (texture, shape, intensity) to achieve state-of-the-art performance. Our approach yields a segmentation Dice coefficient of 93.5% and accuracy of 98.5%, outperforming existing methods by 5-8%, while effectively reducing false positives. Crucially, we leverage XAI, specifically Grad-CAM visualizations with ResNet models, to link nodule morphology-particularly edge-associated vascular patterns-to clinically relevant EGFR and KRAS mutations. This provides unprecedented transparency, bridging AI-driven image analysis with underlying oncogenic mechanisms. Our ResNet-based classifiers achieve high accuracy for mutation prediction (97.6% EGFR, 97.7% KRAS), validated through stratified 10-fold cross-validation. By demystifying model decisions, XAI fosters clinical trust and empowers clinicians to correlate imaging phenotypes with genotypic alterations, paving the way for more precise diagnostics and treatment planning. We also discuss model fairness, computational efficiency, and outline clear future directions for clinical translation.</p>

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AI-Driven Precision Oncology: Integrating Deep Learning, Radiomics, and Genomic Analysis for Enhanced Lung Cancer Diagnosis and Treatment

  • 期刊论文

  • 发表日期：

    2025年6月5日

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  • Faridoddin Shariaty,
  • Vitalii Pavlov,
  • Maksim Baranov

摘要

Lung cancer remains a leading cause of global cancer mortality, demanding improved early detection and personalized therapies. This study introduces an innovative framework integrating deep learning (DL), radiomics, and explainable AI (XAI) to significantly advance lung nodule analysis and predict actionable genetic mutations. We synergize advanced neural networks (U-Net, DeepLabV3 for segmentation; YOLOv7 for detection) with comprehensive radiomic features (texture, shape, intensity) to achieve state-of-the-art performance. Our approach yields a segmentation Dice coefficient of 93.5% and accuracy of 98.5%, outperforming existing methods by 5-8%, while effectively reducing false positives. Crucially, we leverage XAI, specifically Grad-CAM visualizations with ResNet models, to link nodule morphology-particularly edge-associated vascular patterns-to clinically relevant EGFR and KRAS mutations. This provides unprecedented transparency, bridging AI-driven image analysis with underlying oncogenic mechanisms. Our ResNet-based classifiers achieve high accuracy for mutation prediction (97.6% EGFR, 97.7% KRAS), validated through stratified 10-fold cross-validation. By demystifying model decisions, XAI fosters clinical trust and empowers clinicians to correlate imaging phenotypes with genotypic alterations, paving the way for more precise diagnostics and treatment planning. We also discuss model fairness, computational efficiency, and outline clear future directions for clinical translation.