ArticleAnnals of nuclear medicine2022
Deep learning-based detection of parathyroid adenoma by
Article in Annals of nuclear medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 15 citations in OpenAlex.
- Slice Level Classification of Parathyroid Adenoma Using Arterial Phase CT Images with Hybrid Light Attention Mechanism Based Residual Framework.Bioengineering (Basel, Switzerland) · 2026Article
- Article
- Imaging Recommendations for Diagnosis and Management of Primary Parathyroid Pathologies: A Comprehensive Review.Cancers · 2024Review
- A Deep Learning Methodology for the Detection of Abnormal Parathyroid Glands via Scintigraphy withDiseases (Basel, Switzerland) · 2022Article
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Authors and funding
8 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
objectiveIt is important to detect parathyroid adenomas by parathyroid scintigraphy with 99m-technetium sestamibi (
methodsDL-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.
resultsThe 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.
conclusionsThe DL-based models were able to detect parathyroid adenomas with a high sensitivity using parathyroid scintigraphy with 99m-technetium sestamibi.
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