ArticleEuropean radiology2023
FDG metabolic parameter-based models for predicting recurrence after upfront surgery in synchronous colorectal cancer liver metastasis.
Article in European radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Citation and adherence to TRIPOD guidelines by published radiological prognostic models: systematic review.The British journal of radiology · 2025Pooled it
- [Development and validation of a multicenter nomogram predicting the risk of liver metastasis after curative resection of colorectal cancer].Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences · 2026Article
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20 authors.
Funding
Abstract
objectiveThis study aimed to develop and validate post- and preoperative models for predicting recurrence after curative-intent surgery using an FDG PET-CT metabolic parameter to improve the prognosis of patients with synchronous colorectal cancer liver metastasis (SCLM).
methodsIn this retrospective multicenter study, consecutive patients with resectable SCLM underwent upfront surgery between 2006 and 2015 (development cohort) and between 2006 and 2017 (validation cohort). In the development cohort, we developed and internally validated the post- and preoperative models using multivariable Cox regression with an FDG metabolic parameter (metastasis-to-primary-tumor uptake ratio [M/P ratio]) and clinicopathological variables as predictors. In the validation cohort, the models were externally validated for discrimination, calibration, and clinical usefulness. Model performance was compared with that of Fong's clinical risk score (FCRS).
resultsA total of 374 patients (59.1 ± 10.5 years, 254 men) belonged in the development cohort and 151 (60.3 ± 12.0 years, 94 men) in the validation cohort. The M/P ratio and nine clinicopathological predictors were included in the models. Both postoperative and preoperative models showed significantly higher discrimination than FCRS (p < .05) in the external validation (time-dependent AUC = 0.76 [95% CI 0.68-0.84] and 0.76 [0.68-0.84] vs. 0.65 [0.57-0.74], respectively). Calibration plots and decision curve analysis demonstrated that both models were well calibrated and clinically useful. The developed models are presented as a web-based calculator ( https://cpmodel.shinyapps.io/SCLM/ ) and nomograms.
conclusionsFDG metabolic parameter-based prognostic models are well-calibrated recurrence prediction models with good discriminative power. They can be used for accurate risk stratification in patients with SCLM. KEY POINTS: • In this multicenter study, we developed and validated prediction models for recurrence in patients with resectable synchronous colorectal cancer liver metastasis using a metabolic parameter from FDG PET-CT. • The developed models showed good predictive performance on external validation, significantly exceeding that of a pre-existing model. • The models may be utilized for accurate patient risk stratification, thereby aiding in therapeutic decision-making.
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