Fluorescence lifetime imaging for intraoperative cancer delineation in transoral robotic surgery.
Fluorescence lifetime imaging for intraoperative cancer delineation in transoral robotic surgery.
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DOI：
10.1002/tbio.201900017
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发表时间：
2019-12-01
影响因子：
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通讯作者：
Marcu, Laura
中科院分区：
文献类型：
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作者：
Weyers, Brent W;Marsden, Mark;Marcu, Laura
本研究评估了荧光寿命成像（FLIm）在机器人辅助口咽癌手术期间增强术中决策的潜力。使用与da芬奇机器人手术平台集成的定制FLIM仪器，我们首先证明通过组织病理学诊断的上皮组织中的癌症可以与在癌症切除之前在体内成像的周围健康上皮组织以及离体切除标本区分开。其次，我们研究了在手术切除边缘（肿瘤床）的体内成像组织的荧光特性。计算三个光谱通道的荧光寿命和光谱强度比，产生一组六个FLIm参数。目前来自10名接受TORS手术的患者的结果表明，对于至少一个FLIm参数，健康上皮可以从癌症中分离出来（P < .001）。我们还表明，一个多参数线性判别分析方法提供了上级歧视个别FLIM参数成像的组织在体内和体外。总的来说，这项研究突出了FLIM被开发成为口咽部临床癌症应用的诊断工具的潜力。这种技术可以帮助规避与机器人手术平台相关的触觉反馈缺乏所带来的问题，以更好地实现癌症描绘。
This study evaluates the potential for fluorescence lifetime imaging (FLIm) to enhance intraoperative decisionmaking during robotic-assisted surgery of oropharyngeal cancer. Using a custom built FLIm instrument integrated with the da Vinci robotic surgical platform, we first demonstrate that cancer in epithelial tissue diagnosed by histopathology can be differentiated from surrounding healthy epithelial tissue imaged in vivo prior to cancer resection and ex vivo on the excised specimen. Second, we study the fluorescence properties of tissue imaged in vivo at surgical resection margins (tumor bed). Fluorescence lifetimes and spectral intensity ratios were calculated for three spectral channels, producing a set of six FLIm parameters. Current results from 10 patients undergoing TORS procedures demonstrate that healthy epithelium can be resolved from cancer (P < .001) for at least one FLIm parameter. We also showed that a multiparameter linear discriminant analysis approach provides superior discrimination to individual FLIm parameters for tissue imaged both in vivo and ex vivo. Overall, this study highlights the potential for FLIm to be developed into a diagnostic tool for clinical cancer applications of the oropharynx. This technique could help to circumvent the issues posed by the lack of tactile feedback associated with robotic surgical platforms to better enable cancer delineation.