Imaging or Fiber Probe-Based Approach? Assessing Different Methods to Detect Near Infrared Autofluorescence for Intraoperative Parathyroid Identification

成像法还是光纤探针法?评估用于术中甲状旁腺识别的近红外自发荧光检测的不同方法

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Abstract

BACKGROUND: Near infrared autofluorescence (NIRAF) can guide intraoperative parathyroid gland (PG) identification. NIRAF detection devices typically rely on imaging and fiber probe-based approaches. Imaging modalities provide NIRAF pictures on adjacent display monitors, and fiber probe-based systems measure tissue NIRAF and provide real-time quantitative information to objectively aid PG identification. Both device types recently gained FDA approval for PG identification but have never been compared directly. STUDY DESIGN: Patients undergoing thyroidectomy and/or parathyroidectomy were recruited prospectively. Target tissues were intraoperatively visualized with PDE-Neo II (imaging-based) and concurrently assessed with PTeye (fiber probe-based). For PDE-Neo II, NIRAF images were collected from in situ or excised tissues, alongside the surgeon's interpretation of visualized tissues, and retrospectively analyzed in a blinded fashion. The PTeye was concomitantly used to record NIRAF intensities and ratios from the same tissues in real time. RESULTS: Twenty patients were enrolled for concurrent evaluation with both systems, which included 33 PGs and 19 nonparathyroid sites. NIRAF imaging demonstrated 90.9% sensitivity, 73.7% specificity, and 84.6% accuracy for PG identification when interpreted in real time by the surgeon compared with 81.8% sensitivity, 73.7% specificity, and 78.8% accuracy where images were quantitatively analyzed post hoc by an independent observer. In parallel, NIRAF detection with PTeye yielded 97.0% sensitivity, 84.2% specificity, and 92.3% accuracy in real time for the same specimens. CONCLUSIONS: Both NIRAF-based systems were beneficial for identifying PGs intraoperatively. Although NIRAF imaging provides valuable spatial information to localize PGs, NIRAF detection with fiber probe provides real-time quantitative information to identify PGs in presence of ambient room lights.

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