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Development and validation of a LASSO-Based FDG PET/CT model for predicting colorectal adenoma in asymptomatic individuals undergoing colonoscopy

Authors
 Kang, Jeonghyun  ;  Kim, Youngmin  ;  Jeong, Yeongbeom  ;  Lee, Hye Sun  ;  Ryu, Young Hoon  ;  Jeon, Tae Joo  ;  Lee, Jae-Hoon 
Citation
 ANNALS OF NUCLEAR MEDICINE, 2025-11 
Journal Title
ANNALS OF NUCLEAR MEDICINE
ISSN
 0914-7187 
Issue Date
2025-11
Keywords
FDG PET/CT ; Colorectal adenoma ; LASSO ; Colonoscopy ; Risk stratification
Abstract
ObjectiveColonoscopy is the gold standard for colorectal cancer (CRC) screening; however, its invasiveness, cost, and associated risks limit its use in population-wide programs. Therefore, effective noninvasive tools for identifying individuals at high risk for colorectal adenomas-the precursors to CRC-are needed. 2-deoxy-2-[F-1(8)] fluoro-D-glucose positron emission tomography/computed tomography (FDG PET/CT) captures systemic metabolic and inflammatory activity and may offer imaging biomarkers for adenoma risk stratification.MethodsWe retrospectively analyzed 754 asymptomatic individuals who underwent both colonoscopy and FDG PET/CT within 30 days as part of health screening. PET/CT-derived variables included standardized uptake values (SUVs) from visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), skeletal muscle, liver, spleen, bone marrow, and colorectal wall. Clinical data included age, sex, and body mass index (BMI). A least absolute shrinkage and selection operator (LASSO) logistic regression model was trained on 452 individuals and tested in a separate validation cohort of 302.ResultsThe final LASSO model selected eight variables, including VAT area (positive association) and multiple tissue-specific SUV features (negative associations). In the test set, the model achieved an area under the curve (AUC) of 0.693 (95% confidence interval: 0.631-0.754), significantly outperforming individual predictors such as VAT area (AUC = 0.630, P = 0.011), VAT HU (AUC = 0.585, P = 0.001), and SAT SUVmax (AUC = 0.616, P = 0.046). Decision curve analysis demonstrated superior net clinical benefit compared to univariable models.ConclusionA multivariable model integrating FDG PET/CT-derived metabolic features with clinical parameters enables noninvasive prediction of colorectal adenomas. This imaging-based approach may help identify individuals most likely to benefit from colonoscopy, potentially improving the efficiency of CRC screening strategies in opportunistic or high-risk settings.
Full Text
https://link.springer.com/article/10.1007/s12149-025-02122-8
DOI
10.1007/s12149-025-02122-8
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Surgery (외과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Yonsei Biomedical Research Center (연세의생명연구원) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Nuclear Medicine (핵의학교실) > 1. Journal Papers
Yonsei Authors
Kang, Jeonghyun(강정현) ORCID logo https://orcid.org/0000-0001-7311-6053
Ryu, Young Hoon(유영훈) ORCID logo https://orcid.org/0000-0002-9000-5563
Lee, Jae Hoon(이재훈) ORCID logo https://orcid.org/0000-0002-9898-9886
Lee, Hye Sun(이혜선) ORCID logo https://orcid.org/0000-0001-6328-6948
Jeon, Tae Joo(전태주) ORCID logo https://orcid.org/0000-0002-7574-6734
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/209789
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