%0 Journal Article
%T A 18F-FDG PET/CT based radiomics nomogram for predicting disease-free survival in stage II/III colorectal adenocarcinoma.
%A Wang B
%A Hu T
%A Shen R
%A Liu L
%A Qiao J
%A Zhang R
%A Zhang Z
%J Abdom Radiol (NY)
%V 0
%N 0
%D 2024 Aug 3
%M 39096393
暂无%R 10.1007/s00261-024-04515-1
%X <B>OBJECTIVE: </B>This study aimed to establish a clinical nomogram model based on a radiomics signatures derived from 18F-fluorodeoxyglucose positron-emission tomography (18F-FDG PET/CT) and clinical parameters to predict disease-free survival (DFS) in patients with stage II/III colorectal adenocarcinoma. Understanding and predicting DFS in these patients is key to optimizing treatment strategies.<BR><B>METHODS: </B>A retrospective analysis included 332 cases from July 2011 to July 2021 at The Sixth Affiliated Hospital, Sun Yat-sen University, with PET/CT assessing radiomics features and clinicopathological features. Univariate Cox regression, the least absolute shrinkage and selection operator (LASSO) Cox, and multivariable Cox regression identified recurrence-related radiomics features. We used a weighted radiomics score (Rad-score) and independent risk factors to construct a nomogram. Evaluation involved time-dependent receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).<BR><B>RESULTS: </B>The nomogram, incorporating Rad-score, pN, and pT demonstrated robust predictive ability for DFS in stage II/III colorectal adenocarcinoma. Training cohort areas under the curve (AUCs) were 0.78, 0.80, and 0.86 at 1, 2, and 3 years, respectively, and validation cohort AUCs were 0.79, 0.75, and 0.73. DCA and calibration curves affirmed the nomogram's clinical relevance.<BR><B>CONCLUSIONS: </B>The 18F-FDG PET/CT based radiomics nomogram, including Rad-score, pN, and pT, effectively predicted tumor recurrence in stage II/III colorectal adenocarcinoma, significantly enhancing prognostic stratification. Our findings highlight the potential of this nomogram as a guide for clinical decision making to improve patient outcomes.