Background: Accurate and rapid intraoperative tumor margin assessment remains a major challenge in surgical oncology. Current gold-standard methods, such as frozen section histology, are time-consuming, operator-dependent, and prone to misclassification, which limits their clinical utility. Objective: To develop and evaluate a novel hyperspectral imaging (HSI) workflow that integrates deep learning with three-dimensional (3D) tumor modeling for real-time, label-free tumor margin delineation in head and neck squamous cell carcinoma (HNSCC). Methods: Freshly resected HNSCC samples were snap-frozen and imaged ex vivo from multiple perspectives using a standardized HSI protocol, resulting in a 3D model derived from HSI. Each sample was serially sectioned, stained, and annotated by pathologists to create high-resolution 3D histological reconstructions. The volumetric histological models were co-registered with the HSI data (n = 712 Datacubes), enabling voxel-wise projection of tumor segmentation maps from the HSI-derived 3D model onto the corresponding histological ground truth. Three deep learning models were trained and validated on these datasets to differentiate tumor from non-tumor regions with high spatial precision. Results: This work demonstrates strong potential for the proposed HSI system, with an overall classification accuracy of 0.98 and a tumor sensitivity of 0.93, underscoring the system’s ability to reliably detect tumor regions and showing high concordance with histopathological findings. Conclusion: The integration of HSI with deep learning and 3D tumor modeling offers a promising approach for precise, real-time intraoperative tumor margin assessment in HNSCC. This novel workflow has the potential to improve surgical precision and patient outcomes by providing rapid, label-free tissue differentiation.
背景：准确、快速的术中肿瘤切缘评估仍是肿瘤外科领域的主要挑战。当前金标准方法（如冰冻切片组织学检查）耗时较长、依赖操作者经验且易出现误判，限制了其临床应用价值。目的：开发并评估一种新型高光谱成像工作流程，该流程将深度学习与三维肿瘤建模相结合，用于头颈部鳞状细胞癌的实时、无标记肿瘤切缘界定。方法：对新鲜切除的HNSCC样本进行速冻处理，采用标准化HSI方案从多视角进行离体成像，构建基于HSI数据的三维模型。所有样本经连续切片、染色后由病理学家标注，建立高分辨率三维组织学重建模型。将立体组织学模型与HSI数据（n=712个数据立方体）进行空间配准，实现从HSI三维模型到对应组织学金标准的肿瘤分割图谱体素级映射。基于该数据集训练并验证三种深度学习模型，以高空间精度区分肿瘤与非肿瘤区域。结果：本研究证实所提出的HSI系统具有显著应用潜力，总体分类准确率达0.98，肿瘤检测灵敏度达0.93，表明该系统能可靠识别肿瘤区域，且与组织病理学结果高度一致。结论：高光谱成像与深度学习及三维肿瘤建模的融合，为HNSCC术中实现精准、实时的肿瘤切缘评估提供了创新方案。该工作流程通过提供快速、无标记的组织分化识别，有望提升手术精准度并改善患者预后。
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