Yan Bing, Wen Zhining, Xue Lili, Wang Tianyi, Liu Zhichao, Long Wulin, Li Yi, Jing Runyu
State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Department of Head and Neck Oncology Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, China.
College of Chemistry, Sichuan University, Chengdu, China.
Int J Oral Sci. 2025 Jan 26;17(1):12. doi: 10.1038/s41368-025-00346-y.
The presence of a positive deep surgical margin in tongue squamous cell carcinoma (TSCC) significantly elevates the risk of local recurrence. Therefore, a prompt and precise intraoperative assessment of margin status is imperative to ensure thorough tumor resection. In this study, we integrate Raman imaging technology with an artificial intelligence (AI) generative model, proposing an innovative approach for intraoperative margin status diagnosis. This method utilizes Raman imaging to swiftly and non-invasively capture tissue Raman images, which are then transformed into hematoxylin-eosin (H&E)-stained histopathological images using an AI generative model for histopathological diagnosis. The generated H&E-stained images clearly illustrate the tissue's pathological conditions. Independently reviewed by three pathologists, the overall diagnostic accuracy for distinguishing between tumor tissue and normal muscle tissue reaches 86.7%. Notably, it outperforms current clinical practices, especially in TSCC with positive lymph node metastasis or moderately differentiated grades. This advancement highlights the potential of AI-enhanced Raman imaging to significantly improve intraoperative assessments and surgical margin evaluations, promising a versatile diagnostic tool beyond TSCC.
舌鳞状细胞癌（TSCC）中手术切缘阳性的存在显著增加了局部复发的风险。因此，及时、准确地术中评估切缘状态对于确保肿瘤彻底切除至关重要。在本研究中，我们将拉曼成像技术与人工智能（AI）生成模型相结合，提出了一种用于术中切缘状态诊断的创新方法。该方法利用拉曼成像快速、无创地获取组织拉曼图像，然后使用AI生成模型将其转化为苏木精-伊红（H&E）染色的组织病理学图像用于组织病理学诊断。生成的H&E染色图像清晰地显示了组织的病理状况。经三位病理学家独立评审，区分肿瘤组织和正常肌肉组织的总体诊断准确率达到86.7%。值得注意的是，它优于当前的临床实践，尤其是在伴有阳性淋巴结转移或中度分化分级的TSCC中。这一进展凸显了AI增强拉曼成像在显著改善术中评估和手术切缘评估方面的潜力，有望成为TSCC以外的一种通用诊断工具。
