ScholarWorks@성균관대학교

초록

Purpose: The aim of this study was to generate and validate artificial delayed-phase technetium-99m methoxyisobutylisonitrile scintigraphy (aMIBI) images from early-phase technetium-99m methoxyisobutylisonitrile scintigraphy (eMIBI) images. Patients and Methods: This retrospective study included patients with hyperparathyroidism who underwent dual-phase technetium-99m methoxyisobutylisonitrile (MIBI) scintigraphy at 2 centers. The patients were divided into a training set (n = 980), an internal test set (n = 100), and an external test set (n = 253). The generation of aMIBI images from eMIBI images was performed using an unpaired image-to-image translation method. Receiver operating characteristic curves and the area under the curves (AUCs) were used to evaluate the diagnostic performance of aMIBI and eMIBI images in identifying hyperfunctioning parathyroid lesions in both the internal and external test sets. In addition, an artificial intelligence (AI)-assisted diagnostic model combining aMIBI and clinical data was evaluated. Results: The AUCs of aMIBI images were significantly higher than those of eMIBI images (internal test set: 0.944 vs 0.658, P < 0.001; external test set: 0.900 vs 0.761, P < 0.001). The performance of the AI-assisted diagnostic models combining aMIBI images and clinical data was significantly better than those of the aMIBI-only models in both the internal (AUC: 0.974 vs 0.944, P = 0.020) and external (AUC: 0.953 vs 0.900, P < 0.001) test sets. Conclusions: The diagnostic performance of aMIBI images in identifying hyperfunctioning parathyroid lesions was significantly superior to that of eMIBI images in patients with hyperparathyroidism. Models combining aMIBI images with clinical information enhanced the diagnostic performance even further. © 2025 Lippincott Williams and Wilkins. All rights reserved.

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