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近红外自发荧光特征:原发性甲状旁腺功能亢进症中甲状旁腺术中评估的新参数。

Near-Infrared Autofluorescence Signature: A New Parameter for Intraoperative Assessment of Parathyroid Glands in Primary Hyperparathyroidism.

作者信息

Akgun Ege, Ibrahimli Arturan, Berber Eren

机构信息

From the Departments of Endocrine Surgery (Akgun, Ibrahimli, Berber), Cleveland Clinic, Cleveland, OH.

General Surgery (Berber), Cleveland Clinic, Cleveland, OH.

出版信息

J Am Coll Surg. 2025 Jan 1;240(1):84-93. doi: 10.1097/XCS.0000000000001147. Epub 2024 Dec 16.

Abstract

BACKGROUND

The success of parathyroidectomy in primary hyperparathyroidism depends on the intraoperative differentiation of diseased from normal glands. Deep learning can potentially be applied to digitalize this subjective interpretation process that relies heavily on surgeon expertise. In this study, we aimed to investigate whether diseased vs normal parathyroid glands have different near-infrared autofluorescence (NIRAF) signatures and whether related deep learning models can predict normal vs diseased parathyroid glands based on intraoperative in vivo images.

STUDY DESIGN

This prospective study included patients who underwent parathyroidectomy for primary hyperparathyroidism or thyroidectomy using intraoperative NIRAF imaging at a single tertiary referral center from November 2019 to March 2024. Autofluorescence intensity and heterogeneity index of normal vs diseased parathyroid glands were compared, and a deep learning model was developed.

RESULTS

NIRAF images of a total of 1,506 normal and 597 diseased parathyroid glands from 797 patients were analyzed. Normal vs diseased glands exhibited a higher median normalized NIRAF intensity (2.68 [2.19 to 3.23] vs 2.09 [1.68 to 2.56] pixels, p < 0.0001) and lower heterogeneity index (0.11 [0.08 to 0.15] vs 0.18 [0.13 to 0.23], p < 0.0001). On receiver operating characteristics analysis, optimal thresholds to predict a diseased gland were 2.22 in pixel intensity and 0.14 in heterogeneity index. On deep learning, precision and recall of the model were 83.3% each, and area under the precision-recall curve was 0.908.

CONCLUSIONS

Normal and diseased parathyroid glands in primary hyperparathyroidism have different intraoperative NIRAF patterns that could be quantified with intensity and heterogeneity analyses. Visual deep learning models relying on these NIRAF signatures could be built to assist surgeons in differentiating normal from diseased parathyroid glands.

摘要

背景

原发性甲状旁腺功能亢进症甲状旁腺切除术的成功取决于术中区分病变腺体与正常腺体。深度学习有可能应用于将这一严重依赖外科医生专业知识的主观解释过程数字化。在本研究中,我们旨在调查病变甲状旁腺与正常甲状旁腺是否具有不同的近红外自发荧光(NIRAF)特征,以及相关的深度学习模型是否可以根据术中体内图像预测正常甲状旁腺与病变甲状旁腺。

研究设计

这项前瞻性研究纳入了2019年11月至2024年3月在单一三级转诊中心因原发性甲状旁腺功能亢进症接受甲状旁腺切除术或因甲状腺切除术使用术中NIRAF成像的患者。比较了正常甲状旁腺与病变甲状旁腺的自发荧光强度和异质性指数,并开发了一个深度学习模型。

结果

分析了来自797例患者的总共1506个正常甲状旁腺和597个病变甲状旁腺的NIRAF图像。正常腺体与病变腺体表现出更高的标准化NIRAF强度中位数(2.68[2.19至3.23]像素对2.09[1.68至2.56]像素,p<0.0001)和更低的异质性指数(0.11[0.08至0.15]对0.18[0.13至0.23],p<0.0001)。在受试者工作特征分析中,预测病变腺体的最佳阈值为像素强度2.22和异质性指数0.14。在深度学习方面,模型的精度和召回率均为83.3%,精确召回曲线下面积为0.908。

结论

原发性甲状旁腺功能亢进症中的正常甲状旁腺和病变甲状旁腺具有不同的术中NIRAF模式,可通过强度和异质性分析进行量化。可以构建依赖这些NIRAF特征的视觉深度学习模型,以帮助外科医生区分正常甲状旁腺和病变甲状旁腺。

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