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핵의학
- 2026년 09월호
[Thyroid .] CT-Free Quantitative Thyroid SPECT Based on Artificial Intelligence: A Prospective Multicenter Noninferiority Clinical Trial서울의대 / 정현우, 이원우*
- 출처
- Thyroid .
- 등재일
- 2026 Jul
- 저널이슈번호
- 36(7):770-778.
- 내용
Abstract
Background: Hybrid single-photon emission computed tomography (SPECT)/computed tomography (CT) is used for the differential diagnosis of thyrotoxicosis, but its dependence on CT and complex analysis remains debated. This study evaluated whether CT-free thyroid SPECT using artificial intelligence (AI) could substitute for conventional SPECT/CT.Methods: This prospective multicenter noninferiority clinical trial (KCT0008387) included 152 patients with thyrotoxicosis from three tertiary referral hospitals. A pretrained AI model generated technetium thyroid uptake (TcTU) values using SPECT images without CT. These values were compared with TcTU derived from conventional SPECT/CT. The primary endpoint was diagnostic accuracy for Graves' disease. Noninferiority was defined as a lower confidence interval (CI) limit greater than -10%. Secondary endpoints included diagnostic accuracy for destructive thyroiditis and prediction of antithyroid drug (ATD) prescription within one month.
Results: Among 152 patients, 84 had Graves' disease, 45 had indeterminate disease, and 23 had thyroiditis. For Graves' disease, CT-free SPECT demonstrated 86.2% accuracy compared with 85.5% for conventional SPECT/CT. The lower bound of the CI of the accuracy difference was -3.0%, meeting the prespecified noninferiority margin. For thyroiditis and ATD prescription, the lower bounds were -7.4% and -3.2%, respectively. Omission of CT resulted in a 33.5% reduction of radiation exposure from 3.34 mSv to 2.22 mSv, and CT-free SPECT automatically generated TcTU values more quickly than SPECT/CT (40 minutes vs. 4 minutes).
Conclusions: CT-free SPECT using AI demonstrated noninferior diagnostic performance for thyrotoxicosis compared with conventional hybrid imaging while reducing radiation exposure and analysis time.
Affiliations
Hyun Woo Chung 1, Sang-Geon Cho 2, Dongkyu Oh 3 4, Kyounghyoun Kwon 5, Ji Hye Kim 3, Young So 1, Jae Hoon Moon 6, Soyeon Ahn 7, Won Woo Lee 3 4 8 9; K-SPECT Group
1Department of Nuclear Medicine, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul, Republic of Korea.
2Department of Nuclear Medicine, Chonnam National University Hospital and Medical School, Gwangju, Republic of Korea.
3Department of Nuclear Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.
4Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.
5BioDrone Research Institute, MDimune Inc., Seoul, Republic of Korea.
6Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.
7Medical Research Collaborating Center, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.
8Institute of Radiation Medicine, Medical Research Center, Seoul National University, Seoul, Republic of Korea.
9Department of Health Science and Technology, The Graduate School of Convergence Science and Technology, Seoul National University, Suwon, Republic of Korea.
- 키워드
- Graves’; artificial intelligence; computed tomography; disease; noninferiority clinical trial; single-photon emission computed tomography; thyroiditis; thyrotoxicosis.
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편집위원
Thyrotoxicosis 진단에서 Thyroid SPECT/CT에서 CT를 획득하지 않고, artificial intelligence 기법을 사용하면, conventional SPECT/CT 진단성능을 대처할 수 있는지를 평가한 임상연구임. 연구결과 artificial intelligence 기법을 도입한 SPECT 만으로 SPECT/CT와 동등한 성적을 보이며, 방사선피폭을 줄일 수 있으며, Tc uptake 평가에 시간이 단축됨을 보여줌. 해당 연구결과는 갑상선 진료 임상가와 핵의학 전문가가 관심을 가질 만한 연구로 생각됨.
덧글달기닫기2026-09-09 10:44:41
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