Evidence map›Paper›PMID 40172768›Full record

ArticleAnnals of nuclear medicine2025

Analysis of the best time-point for 18F-FDG PET/CT delayed imaging in patients of small colorectal cancer liver metastasis with hypothyroidism based on diagnostic efficacy and image standardized uptake values.

Yusong Pei, Yanan Tong, Zhiguo Wang, Xinxin Qiao, Yanqing Liu, Guoxu Zhang

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Article in Annals of nuclear medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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6 authors.

Yusong PeiDepartment of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, China.
Yanan TongDepartment of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, China.
Zhiguo WangDepartment of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, China.
Xinxin QiaoDepartment of Radiology, The Peoples Hospital of China Medical University, Shenyang, China.
Yanqing LiuDepartment of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, China.
Guoxu ZhangDepartment of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, China. zhangguoxu_502@163.com.

Funding

Liaoning Applied Basic Research Programme 2022JH2/101500011Liaoning Provincial Science and Technology Programme Joint Programme 2024JH2/102600273
6 · The paper itself

Abstract

objectiveThis study analyzes the role of positron emission tomography/computed tomography (PET/CT) in the diagnosis of small (< 10 mm) colorectal cancer liver metastasis (CRLM) lesions in patients with hypothyroidism. In particular, the impact of the best time for delayed imaging on improving diagnostic efficacy.

methodsWe retrospectively analyzed 231 patients with small CRLM lesions with hypothyroidism who underwent dual time-point 18F-FDG PET/CT imaging. Based on the previous studies and clinical practice experience, 120-190 min was selected as the time range for delayed imaging, divided into eight teams in 10-min groups. The delayed images of the eight time periods were first analyzed and compared for diagnostic efficacy, and second analyzed and compared for standardized uptake value (SUV) and of PET/CT images to observe the trend of SUV values over time.

resultsThe results of diagnostic efficacy analysis indicated that the 180-min delay group had the highest diagnostic efficacy (sensitivity, specificity, and accuracy). Comparison of the SUV values with the delay time analysis showed that maximum standardized uptake value (SUVmax) increased with the delay time, and the normal liver tissue (SUVmean) decreased with the delay time, which resulted in the gradual increase in the ratio of the lesion to the normal liver tissue (TNR). By selecting the time-point with the highest TNR ratio and stable SUV value, and combining the results of diagnostic efficacy, this study successfully verified the best imaging time-point. After comprehensive consideration, 180 min was determined as the best imaging time-point, when the TNR reached the highest, the SUV value was stable, and the diagnostic efficacy was best.

conclusionsIn this study, the impact of delayed imaging on the diagnostic efficacy and SUV value of PET/CT images in patients of small CRLM with hypothyroidism was shown intuitively, and the changing pattern of SUV at different time points was also observed. The best time-point for PET/CT delayed imaging was determined to be 180 min, which provides a new scanning program for the diagnosis in patients of small CRLM with hypothyroidism.

Indexed as

Colorectal NeoplasmsFluorodeoxyglucose F18HypothyroidismLiver NeoplasmsPositron Emission Tomography Computed TomographyAdultAgedAged, 80 and overBiological TransportFemaleHumansMaleMiddle AgedRetrospective StudiesTime FactorsFluorodeoxyglucose F1818F-FDG PET/CTColorectal cancer liver metastasisDelayed imagingDiagnostic efficacyHypothyroidism

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.