ArticleCancer imaging : the official publication of the International Cancer Imaging Society2026
Krenning score enhances integrated biomarker models for survival prediction in PRRT-treated GEP-NET: a retrospective cohort study.
Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2026. 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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Abstract
backgroundPurpose of this study is to evaluate the value of a routinely available baseline SSTR-PET imaging parameter, the Krenning score (KS), within integrated biomarker models for overall survival prediction in gastroenteropancreatic neuroendocrine tumor (GEP-NET) patients undergoing peptide receptor radionuclide therapy (PRRT).
methodsWe retrospectively analyzed 178 patients with GEP-NET who underwent PRRT. At baseline, the KS, clinical, histopathological, and laboratory parameters were integrated and correlated with OS. OS predictors were identified using univariate Cox regression analysis and incorporated into multivariate models. Model performance was assessed using the concordance index (C-index) and Akaike information criterion (AIC).
resultsIn univariate analysis, the following parameters were significantly associated with shorter OS: KS 3 vs. KS 4 (p = 0.042), CgA > 155 ng/mL (p = 0.003), NSE > 35 ng/mL (p = 0.042), BMI < 18.5 kg/m² (p = 0.017), CRP > 0.5 mg/dL (p = 0.045), albumin < 4.1 g/dL (p = 0.017), and Hb < 12 g/dL (p = 0.017). The multivariate Cox regression model including KS, hemoglobin, and NSE showed the lowest AIC (C-index = 0.64, CI: 0.57-0.71; AIC = 527.60), while the model incorporating BMI, hemoglobin, and CgA demonstrated the highest C-index (C-index = 0.66, CI: 0.60-0.72; AIC = 582.82).
conclusionIntegrated baseline biomarker models combining clinical, laboratory, and imaging parameters may support overall survival risk stratification in GEP-NET patients undergoing PRRT. Incorporation of the routinely available Krenning score contributed to one of the best-performing parsimonious exploratory multivariable models, suggesting that accessible molecular imaging parameters may complement established baseline biomarkers. These exploratory findings require validation in larger, preferably multicenter, independent cohorts before clinical implementation. CLINICAL TRIAL NUMBER: Not applicable.
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