Prediction of overall survival in patients with colorectal cancer using baseline 18F-FDG PET/CT radiomics
Year: 2026
Type: article
Study design: Observational Study
Abstract: To predict 3-year overall survival (OS) in patients with colorectal cancer (CRC) using baseline 18 F-FDG PET/CT radiomics. This retrospective study entrolled 166 patients with colorectal cancer (CRC) , who were randomly assigned into a training cohort ( n = 133) and an internal random hold-out validation cohort ( n = 33). The optimum subset was screened out from 2446 radiomic features via least absolute shrinkage and selection operator (LASSO) Cox regression. Significant clinical characteristics identified by Cox analysis were combined with radiomic score (Radscore) to construct a combined prediction model. The Akaike information criterion (AIC), Harrell’s concordance index (C-index) and the area under the receiver operating characteristic curve (AUC) were applied to evaluate model performance. The complex model had the lowest AIC, highest C-index with optimal efficacy across both cohorts. In the training cohort, its AUC was superior to the single radiomics (0.877 vs. 0.784, p = 0.027) and clinical model (0.877 vs. 0.774, p < 0.001), with no significant difference between the two alone (0.784 vs. 0.774, p = 0.878). In the internal validation cohort, its AUC differed significantly from the clinical model (0.896 vs. 0.822, p = 0.012). By contrast, no significant differences were found between the radiomics model and the complex model (0.707 vs. 0.896, p = 0.094), nor between the radiomics model and the clinical model (0.707 vs. 0.822, p = 0.400). Baseline PET/CT radiomics enables effective prediction of 3-year OS in patients with CRC. The complex model could further improve the predictive efficacy of the clinical model alone.
Source: Scientific Reports
Authors: Na Wang, Huanxin Zhu, Meng Dai, Yunuan Liu, Fenglian Jing, Yuhan Sun, Jingmian Zhang, Jianfang Wang, Xinming Zhao, Zhaoqi Zhang
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,690
Award: G617940166
OpenAlex ID: W7212153522
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