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Deep learning-based detection of parathyroid adenoma by 99mTc-MIBI scintigraphy in patients with primary hyperparathyroidism

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Abstract

Objective

It is important to detect parathyroid adenomas by parathyroid scintigraphy with 99m-technetium sestamibi (99mTc-MIBI) before surgery. This study aimed to develop and validate deep learning (DL)-based models to detect parathyroid adenoma in patients with primary hyperparathyroidism, from parathyroid scintigrams with 99mTc-MIBI.

Methods

DL-based models for detecting parathyroid adenoma in early- and late-phase parathyroid scintigrams were, respectively, developed and evaluated. The training dataset used to train the models was collected from 192 patients (165 adenoma cases, mean age: 64 years ± 13, 145 women) and the validation dataset used to tune the models was collected from 45 patients (30 adenoma cases, mean age: 67 years ± 12, 37 women). The images were collected from patients who were pathologically diagnosed with parathyroid adenomas or in whom no lesions could be detected by either parathyroid scintigraphy or ultrasonography at our institution from June 2010 to March 2019. The models were tested on a dataset collected from 44 patients (30 adenoma cases, mean age: 67 years ± 12, 38 women) who took scintigraphy from April 2019 to March 2020. The models’ lesion-based sensitivity and mean false positive indications per image (mFPI) were assessed with the test dataset.

Results

The sensitivity was 82% [95% confidence interval 72–92%] with mFPI of 0.44 for the scintigrams of the early-phase model and 83% [73–92%] with mFPI of 0.31 for the scintigrams of the delayed-phase model in the test dataset, respectively.

Conclusions

The DL-based models were able to detect parathyroid adenomas with a high sensitivity using parathyroid scintigraphy with 99m-technetium sestamibi.

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Acknowledgements

This manuscript has been proofread by Mrs. Shannon L. Walston.

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Authors and Affiliations

  1. Department of Nuclear Medicine, Graduate School of Medicine, Osaka City University, 1-4-3, Asahimachi, Abeno-ku, Osaka, 545-8585, Japan

    Atsushi Yoshida, Shigeaki Higashiyama & Joji Kawabe

  2. Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka City University, 1-4-3, Asahimachi, Abeno-ku, Osaka, 545-8585, Japan

    Daiju Ueda, Toshimasa Matsumoto & Yukio Miki

  3. Department of Radiology, Osaka City University Hospital, 1-5-7, Asahimachi, Abeno-ku, Osaka, 545-8586, Japan

    Yutaka Katayama & Takashi Yamanaga

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  1. Atsushi Yoshida
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Correspondence to Daiju Ueda.

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Yoshida, A., Ueda, D., Higashiyama, S. et al. Deep learning-based detection of parathyroid adenoma by 99mTc-MIBI scintigraphy in patients with primary hyperparathyroidism. Ann Nucl Med 36, 468–478 (2022). https://doi.org/10.1007/s12149-022-01726-8

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