Intraoperative Mapping of Parathyroid Glands Using Fluorescence Lifetime Imaging.
Intraoperative Mapping of Parathyroid Glands Using Fluorescence Lifetime Imaging.
复制标题
利用荧光寿命成像技术进行甲状旁腺术中定位 。
DOI：
10.1016/j.jss.2021.03.023
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发表时间：
2021-09
期刊：
影响因子：
--
通讯作者：
Campbell MJ
中科院分区：
文献类型：
--
作者：
Marsden M;Weaver SS;Marcu L;Campbell MJ
甲状旁腺功能减退是甲状腺切除术后的常见并发症。目前需要借助技术手段帮助外科医生识别甲状旁腺。与近红外技术不同，荧光寿命成像（FLIm）不受环境光影响，在识别甲状旁腺组织方面可能具有重要价值，但尚未有人以此为目的对其进行评估。
我们采用FLIm技术，对21例接受甲状腺和甲状旁腺手术患者的甲状腺、甲状旁腺、淋巴组织和脂肪组织进行紫外线（355纳米）激发下的时间分辨自发荧光特征测量（3个光谱发射通道的平均荧光寿命）。运用曼-惠特尼U检验评估FLIm区分正常细胞甲状旁腺与其他各类组织的能力。随后，通过留一法交叉验证，对多种机器学习分类器（随机森林、神经网络、支持向量机）识别甲状旁腺的能力进行评估。
结果显示，在光谱通道2和通道3中，甲状旁腺与其他各类组织的平均荧光寿命存在统计学显著差异。其中，脂肪组织与甲状旁腺之间的差异最为明显（p<0.001）；甲状旁腺与淋巴组织（p<0.05）以及甲状旁腺与甲状腺（p<0.01）之间的差异虽相对较小，但仍具有统计学意义。基于平均荧光寿命训练的随机森林分类器在采集运行层面检测甲状旁腺组织的灵敏度达到100%，特异度达到93%。
研究发现，基于FLIm技术得出的参数能够区分甲状旁腺与其他相邻组织类型，有望用于手术视野扫描，实时识别甲状旁腺组织。
Hypoparathyroidism is a common complication following thyroidectomy. There is a need for technology to aid surgeons in identifying the parathyroid glands. In contrast to near infrared technologies, fluorescence lifetime imaging (FLIm) is not affected by ambient light and may be valuable in identifying parathyroid tissue, but has never been evaluated in this capacity. We used FLIm to measure the UV induced (355 nm) time-resolved autofluorescence signatures (average lifetimes in 3 spectral emission channels) of thyroid, parathyroid, lymphoid and adipose tissue in 21 patients undergoing thyroid and parathyroid surgery. The Mann-Whitney U test was used to assess the ability of FLIm to discriminate normocellular parathyroid from each of the other tissues. Various machine learning classifiers (random forests, neural network, support vector machine) were then evaluated to recognize parathyroid through a leave-one-out cross-validation. Statistically significant differences in average lifetime were observed between parathyroid and each of the other tissue types in spectral channels 2 and 3 respectively. The largest change was observed between adipose tissue and parathyroid (p<0.001), while less pronounced but still significant changes were observed when comparing parathyroid with lymphoid tissue (p<0.05) and thyroid (p<0.01). A random forest classifier trained on average lifetimes was found to detect parathyroid tissue with 100% sensitivity and 93% specificity at the acquisition run level. We found that FLIm derived parameters can distinguish the parathyroid glands and other adjacent tissue types and has promise in scanning the surgical field to identify parathyroid tissue in real-time.
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影响因子：
3.9
作者：
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