基于深度学习的高光谱技术在口腔鳞状细胞癌转移淋巴结中的识别-一项初步研究

Deep learning-based hyperspectral technique identifies metastatic lymph nodes in oral squamous cell carcinoma-A pilot study

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ORAL DISEASES

影响因子:2.9

分区:医学3区 / 牙科与口腔外科4区

发表日期:2025 Feb

作者: Qingxiang Li, Xueyu Zhang, Jianyun Zhang, Hongyuan Huang, Liangliang Li, Chuanbin Guo, Wei Li, Yuxing Guo

DOI: 10.1111/odi.15067

摘要

旨在建立一个基于高光谱成像与深度学习的系统，用于检测转移淋巴结中的癌细胞。收集了45例口腔鳞状细胞癌（OSCC）患者的转移淋巴结连续切片。建立了一种改进的ResUNet算法，用于分析癌细胞与淋巴细胞之间的光谱曲线差异，以及肿瘤组织与正常组织之间的差异。研究发现，利用高光谱图像可以区分转移淋巴结中的癌细胞、淋巴细胞和红细胞，整体准确率（OA）为87.30%，平均准确率（AA）为85.46%。癌变区域可以通过高光谱图像和深度学习识别，平均交并比（IOU）为0.6253，准确率为0.7692。这项研究表明，基于深度学习的高光谱技术能够识别OSCC转移淋巴结中的肿瘤组织，达到较高的病理诊断准确性，提高工作效率并减轻工作负担，但这些结果仍处于初步阶段，样本量有限。

Abstract

To establish a system based on hyperspectral imaging and deep learning for the detection of cancer cells in metastatic lymph nodes.The continuous sections of metastatic lymph nodes from 45 oral squamous cell carcinoma (OSCC) patients were collected. An improved ResUNet algorithm was established for deep learning to analyze the spectral curve differences between cancer cells and lymphocytes, and that between tumor tissue and normal tissue.It was found that cancer cells, lymphocytes, and erythrocytes in the metastatic lymph nodes could be distinguished basing hyperspectral image, with overall accuracy (OA) as 87.30% and average accuracy (AA) as 85.46%. Cancerous area could be recognized by hyperspectral image and deep learning, and the average intersection over union (IOU) and accuracy were 0.6253 and 0.7692, respectively.This study indicated that deep learning-based hyperspectral techniques can identify tumor tissue in OSCC metastatic lymph nodes, achieving high accuracy of pathological diagnosis, high work efficiency, and reducing work burden. But these are preliminary results limited to a small sample.