Department of Endocrine Surgery, Cleveland Clinic, OH.
Department of Endocrine Surgery, Cleveland Clinic, OH; Department of General Surgery, Cleveland Clinic, OH.
Surgery. 2021 Aug;170(2):454-461. doi: 10.1016/j.surg.2021.01.033. Epub 2021 Mar 3.
Previous work showed that normal and abnormal parathyroid glands exhibit different patterns of autofluorescence, with the former appearing brighter and more homogenous. However, an objective algorithm based on quantified measurements was not provided. The aim of this study is to develop objective algorithms for intraoperative autofluorescence assessment of parathyroid glands in primary hyperparathyroidism using artificial intelligence.
The utility of near-infrared fluorescence imaging in parathyroidectomy procedures was evaluated in a study approved by the institutional review board. Autofluorescence patterns of parathyroid glands were measured intraoperatively. Comparisons were performed between normal and abnormal glands, as well as between different pathologies. Using machine learning, decision trees were created.
Normal parathyroid glands were brighter (higher normalized autofluorescence pixel intensity) and more homogenous (lower heterogeneity index) compared to abnormal glands. Optimal cutoffs to differentiate normal from abnormal parathyroid glands were >2.0 for normalized autofluorescence intensity (sensitivity 73%, specificity 70%, area under the curve 0.756) and <0.12 for parathyroid heterogeneity index (sensitivity 75%, specificity 81%, area under the curve 0.839). Decision trees created by machine learning using normalized autofluorescence intensity, heterogeneity index, and gland volume were 95% accurate in predicting normal versus abnormal glands and 84% accurate in predicting subclasses of parathyroid pathologies.
To our knowledge, this is the first study to date reporting objective algorithms using quantified autofluorescence data to intraoperatively assess parathyroid glands in primary hyperparathyroidism. These results suggest that objective data can be obtained from autofluorescence signals to help differentiate abnormal parathyroid glands from normal glands.
先前的研究表明，正常和异常甲状旁腺的自发荧光呈现不同的模式，前者的荧光更亮且更均匀。然而，并未提供基于量化测量的客观算法。本研究旨在使用人工智能为原发性甲状旁腺功能亢进症中的甲状旁腺术中自发荧光评估开发客观算法。
这项经机构审查委员会批准的研究评估了近红外荧光成像在甲状旁腺切除术过程中的应用。术中测量甲状旁腺的自发荧光模式。比较了正常和异常腺体之间，以及不同病变之间的差异。使用机器学习创建决策树。
与异常腺体相比，正常甲状旁腺的荧光强度更高（归一化自发荧光像素强度更高），且更均匀（异质性指数更低）。区分正常和异常甲状旁腺的最佳截断值为归一化自发荧光强度>2.0（灵敏度 73%，特异性 70%，曲线下面积 0.756）和甲状旁腺异质性指数<0.12（灵敏度 75%，特异性 81%，曲线下面积 0.839）。使用机器学习基于归一化自发荧光强度、异质性指数和腺体体积创建的决策树在预测正常与异常腺体方面的准确率为 95%，在预测甲状旁腺病变的亚类方面的准确率为 84%。
据我们所知，这是迄今为止第一项报告使用量化自发荧光数据在原发性甲状旁腺功能亢进症中术中评估甲状旁腺的客观算法的研究。这些结果表明，可以从自发荧光信号中获得客观数据，以帮助区分异常甲状旁腺和正常腺体。
