Trial reportJournal of translational medicine2022
Radiomic analysis for predicting prognosis of colorectal cancer from preoperative
Trial report in Journal of translational medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- Review
- CT-Based Radiomic Signatures Associated with Serum CEA Status in Colon Cancer.Diagnostics (Basel, Switzerland) · 2026Article
- Machine Learning-Based Pathomics Signature in Predicting MSH2 Expression and Prognosis in Gastric Cancer.Clinical and translational gastroenterology · 2026Article
- Clinical utility and future directions of FDG-PET in rectal cancer management.Annals of nuclear medicine · 2026Review
- Development and Validation of Time-to-Event Machine Learning Models for Predicting Disease-Free Survival in Patients with Locally Advanced Colorectal Cancer: A Multicenter Cohort Study.Annals of surgical oncology · 2026Article
- Magnetic resonance imaging based radiomics for predicting pathogenetic features and survival in rectal cancer.Pathology oncology research : POR · 2026Article
- Divergent Prognostic Value of Primary Tumor Segmentation Metrics on Baseline FDG PET/CT in Colorectal Cancer.Cancers · 2025Article
- Review
- Machine learning-based MRI radiomics predict IL18 expression and overall survival of low-grade glioma patients.NPJ precision oncology · 2025Article
- Convergence of evolving artificial intelligence and machine learning techniques in precision oncology.NPJ digital medicine · 2025Article
- Article
- AAbdominal radiology (New York) · 2025Article
- Added value of dual-phaseFrontiers in oncology · 2025Article
- Prognostic effect of CD74 and development of a radiomic model for predicting CD74 expression in non-small cell lung cancer.Frontiers in medicine · 2025Article
- Role of molecular imaging in prognosis, diagnosis, and treatment of gastrointestinal cancers: An update on new therapeutic methods.World journal of methodology · 2024Review
- Correlation between abdominal computed tomography signs and postoperative prognosis for patients with colorectal cancer.World journal of gastrointestinal surgery · 2024Article
- [Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Review
- Predictive value of delta-radiomic features for prognosis of advanced non-small cell lung cancer patients undergoing immune checkpoint inhibitor therapy.Translational lung cancer research · 2024Article
- Effectiveness of CT radiomic features combined with clinical factors in predicting prognosis in patients with limited-stage small cell lung cancer.BMC cancer · 2024Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
backgroundTo develop and validate a survival model with clinico-biological features and
methodsA total of 196 pathologically confirmed patients with colorectal cancer (stage I to stage IV) were included. Preoperative clinical factors, serum tumor markers, and PET/CT radiomic features were included for the recurrence-free survival analysis. For the modeling and validation, patients were randomly divided into the training (n = 137) and validation (n = 59) set, while the 78 stage III patients [training (n = 55), and validation (n = 23)] was divided for the further experiment. After selecting features by the log-rank test and variable-hunting methods, random survival forest (RSF) models were built on the training set to analyze the prognostic value of selected features. The performance of models was measured by C-index and was tested on the validation set with bootstrapping. Feature importance and the Pearson correlation were also analyzed.
resultsRadiomics signature (containing four PET/CT features and four clinical factors) achieved the best result for prognostic prediction of 196 patients (C-index 0.780, 95% CI 0.634-0.877). Moreover, four features (including two clinical features and two radiomics features) were selected for prognostic prediction of the 78 stage III patients (C-index was 0.820, 95% CI 0.676-0.900). K-M curves of both models significantly stratified low-risk and high-risk groups (P < 0.0001). Pearson correlation analysis demonstrated that selected radiomics features were correlated with tumor metabolic factors, such as SUVmean, SUVmax.
conclusionThis study presents integrated clinico-biological-radiological models that can accurately predict the prognosis in colorectal cancer using the preoperative
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