融合多模态影像与基因组信息，用深度学习提前发现癌症
Deep Learning-Based Computer Vision Models for Early Cancer Detection Using Multimodal Medical Imaging and Radiogenomic Integration Frameworks
- 用CNN、Transformer等模型分析MRI、CT等多种医学影像
- 通过影像与基因数据关联，预测肿瘤基因型和耐药性
- 无需活检即可评估癌症分子特征，适合临床早期筛查
早期癌症检测是现代医疗的核心挑战，诊断延迟会显著降低生存率。深度学习在医学影像分析中取得突破，卷积神经网络（CNN）、Transformer及混合注意力架构可自动提取多模态影像（包括MRI、CT、PET、乳腺摄影、病理切片和超声）中的复杂空间、形态和时间模式。这些模型能识别肉眼不可见的微小组织异常和肿瘤微环境变化，超越传统放射学评估。更进一步，将多模态影像与放射基因组学结合，实现影像特征与基因组、转录组及表观遗传生物标志物的关联，开创个性化肿瘤学新范式。该融合方法可在不进行侵入性活检的情况下，预测肿瘤基因型、免疫反应、分子亚型和治疗耐药性。
原文摘要 · Abstract (English)
Early cancer detection remains one of the most critical challenges in modern healthcare, where delayed diagnosis significantly reduces survival outcomes. Recent advancements in artificial intelligence, particularly deep learning, have enabled transformative progress in medical imaging analysis. Deep learning-based computer vision models, such as convolutional neural networks (CNNs), transformers, and hybrid attention architectures, can automatically extract complex spatial, morphological, and temporal patterns from multimodal imaging data including MRI, CT, PET, mammography, histopathology, and ultrasound. These models surpass traditional radiological assessment by identifying subtle tissue abnormalities and tumor microenvironment variations invisible to the human eye. At a broader scale, the integration of multimodal imaging with radiogenomics linking quantitative imaging features with genomics, transcriptomics, and epigenetic biomarkers has introduced a new paradigm for personalized oncology. This radiogenomic fusion allows the prediction of tumor genotype, immune response, molecular subtypes, and treatment resistance without invasive biopsies.
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