Significance: Raman spectroscopy has been developed for surgical guidance applications interrogating live tissue during tumor resection procedures to detect molecular contrast consistent with cancer pathophysiological changes. To date, the vibrational spectroscopy systems developed for medical applications include single-point measurement probes and intraoperative microscopes. There is a need to develop systems with larger fields of view (FOVs) for rapid intraoperative cancer margin detection during surgery. Aim: We design a handheld macroscopic Raman imaging system for in vivo tissue margin characterization and test its performance in a model system. Approach: The system is made of a sterilizable line scanner employing a coherent fiber bundle for relaying excitation light from a 785-nm laser to the tissue. A second coherent fiber bundle is used for hyperspectral detection of the fingerprint Raman signal over an area of 1 cm2. Machine learning classifiers were trained and validated on porcine adipose and muscle tissue. Results: Porcine adipose versus muscle margin detection was validated ex vivo with an accuracy of 99% over the FOV of 95 mm2 in ∼3 min using a support vector machine. Conclusions: This system is the first large FOV Raman imaging system designed to be integrated in the workflow of surgical cancer resection. It will be further improved with the aim of discriminating brain cancer in a clinically acceptable timeframe during glioma surgery.

中文翻译：

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用于基于机器学习的分子组织边缘表征的手持式宏观拉曼光谱成像仪

意义：拉曼光谱已被开发用于在肿瘤切除过程中询问活组织以检测与癌症病理生理变化一致的分子对比度的手术指导应用。迄今为止，为医疗应用开发的振动光谱系统包括单点测量探头和术中显微镜。需要开发具有更大视野 (FOV) 的系统，以便在手术期间快速检测术中癌症边缘。目的：我们设计了一种手持式宏观拉曼成像系统，用于体内组织边缘表征，并在模型系统中测试其性能。方法：该系统由可消毒的线扫描仪组成，该扫描仪采用相干光纤束将来自 785 nm 激光器的激发光传递到组织。第二个相干光纤束用于对 1 cm2 区域内的指纹拉曼信号进行高光谱检测。机器学习分类器在猪脂肪和肌肉组织上进行了训练和验证。结果：使用支持向量机在约 3 分钟内验证了猪脂肪与肌肉边缘检测的离体精度，超过 95 mm2 的 FOV，精度为 99%。结论：该系统是第一个大型 FOV 拉曼成像系统，旨在集成到手术癌症切除的工作流程中。它将进一步改进，目的是在胶质瘤手术期间在临床可接受的时间范围内区分脑癌。使用支持向量机在约 3 分钟内验证了猪脂肪与肌肉边缘检测的离体精度，超过 95 mm2 的 FOV，准确度为 99%。结论：该系统是第一个大型 FOV 拉曼成像系统，旨在集成到手术癌症切除的工作流程中。它将进一步改进，目的是在胶质瘤手术期间在临床可接受的时间范围内区分脑癌。使用支持向量机在约 3 分钟内验证了猪脂肪与肌肉边缘检测的离体精度，超过 95 mm2 的 FOV，准确度为 99%。结论：该系统是第一个大型 FOV 拉曼成像系统，旨在集成到手术癌症切除的工作流程中。它将进一步改进，目的是在胶质瘤手术期间在临床可接受的时间范围内区分脑癌。