Tian Yudong, Zhao Xiangyu, Shao Jingzhu, Xue Bingsen, Huang Lianting, Kang Yani, Li Hanyue, Liu Gang, Yang Haitang, Wu Chongzhao
Center for Biophotonics, Institute of Medical Robotics, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.
Analyst. 2025 Aug 18;150(17):3800-3811. doi: 10.1039/d5an00216h.
Lung cancer is one of the most prevalent malignancies, characterized by high morbidity and mortality rates. Current diagnostic approaches primarily rely on CT imaging and histopathological evaluations, which are time-consuming, heavily dependent on pathologists' expertise, and prone to misdiagnosis. Fourier transform infrared (FTIR) microspectroscopy is a promising label-free technique that can offer insights into morphological and molecular pathological alterations in biological tissues. Here, we present a novel FTIR microspectroscopy method enhanced by a deep learning model for differentiating lung cancer tissues, which serves as a crucial adjunct to clinical diagnosis. We propose an infrared spectral domain adversarial neural network (IRS-DANN), which employs a domain adversarial learning mechanism to mitigate the impact of inter-patient variability, thereby enabling the accurate discrimination of lung cancer tissues. This method demonstrates superior classification performance on a real clinical FTIR dataset, even with limited training samples. Additionally, we visualize and elucidate the FTIR fingerprint peaks, which are linked to the corresponding biological components and crucial for lung cancer differentiation. These findings highlight the great potential of incorporating FTIR microspectroscopy with the deep learning model as a valuable tool for the diagnosis and pathological studies of lung cancer.
肺癌是最常见的恶性肿瘤之一，其特点是发病率和死亡率高。目前的诊断方法主要依赖于CT成像和组织病理学评估，这些方法耗时、严重依赖病理学家的专业知识，并且容易误诊。傅里叶变换红外（FTIR）显微光谱技术是一种很有前景的无标记技术，能够深入了解生物组织中的形态学和分子病理学改变。在此，我们提出一种通过深度学习模型增强的新型FTIR显微光谱方法，用于鉴别肺癌组织，该方法可作为临床诊断的重要辅助手段。我们提出了一种红外光谱域对抗神经网络（IRS-DANN），它采用域对抗学习机制来减轻患者间变异性的影响，从而能够准确鉴别肺癌组织。即使在训练样本有限的情况下，该方法在真实临床FTIR数据集上也表现出卓越的分类性能。此外，我们对FTIR指纹峰进行了可视化和阐释，这些指纹峰与相应的生物成分相关联，对肺癌鉴别至关重要。这些发现凸显了将FTIR显微光谱技术与深度学习模型相结合作为肺癌诊断和病理研究的宝贵工具的巨大潜力。
