ArticleAnnals of nuclear medicine2026
Development and validation of a LASSO-Based FDG PET/CT model for predicting colorectal adenoma in asymptomatic individuals undergoing colonoscopy.
Article in Annals of nuclear medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Prognostic significance and pathological correlation analysis of DKK4 in colorectal cancer.Translational cancer research · 2026Article
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Authors and funding
7 authors.
Funding
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-[¹⁸F] 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.
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Registered trials
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