ArticleCancer imaging : the official publication of the International Cancer Imaging Society2023
Multi-center evaluation of machine learning-based radiomic model in predicting disease free survival and adjuvant chemotherapy benefit in stage II colorectal cancer patients.
Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Radiomics in precision medicine for colorectal cancer: a bibliometric analysis (2013-2023).Frontiers in oncology · 2024Pooled it
- CT-based radiomics signature for optimizing adjuvant chemotherapy decisions in stage II colorectal cancer with proficient mismatch repair.Abdominal radiology (New York) · 2026Article
- A Vision Transformer- and Radiomics-based Model for Predicting Liver Metastasis-Free Survival in Patients with Rectal Cancer.Radiology. Imaging cancer · 2026Article
- A study on screening of postoperative chemotherapy beneficial populations in stage II colorectal cancer based on diffusion weighted imaging andJournal of gastrointestinal oncology · 2026Article
- Development and external validation of an interpretable multimodal deep learning model for 5-year mortality in high-risk stage ii colorectal cancer.International journal of colorectal disease · 2026Article
- 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
- Web-based explainable machine-learning tool for predicting five-year recurrence of colorectal cancer after curative resection: multicentre retrospective cohort study.Discover oncology · 2025Article
- Review
- Mathematical Modeling and Association Analysis Decipher the Impact of the Gut Microbiome on Cancer Immunotherapy.Cancer research · 2025Article
- Immunophenotype-guided interpretable radiomics model for predicting neoadjuvant anti-PD-1 response in stage III-IV d-MMR/MSI-H colorectal cancer.Journal for immunotherapy of cancer · 2025Article
- An interpretable CT-based machine learning model for predicting recurrence risk in stage II colorectal cancer.Insights into imaging · 2025Article
- Radiomic study of common sellar region lesions differentiation in magnetic resonance imaging based on multi-classification machine learning model.BMC medical imaging · 2025Article
- Chronic Inflammation Index-Based Tumor Subsite Classification Correlated with Chemotherapy Benefit and Survival Outcomes in Stage II-III Colorectal Cancer.Journal of inflammation research · 2025Article
- Integrating Clinical Data and Radiomics and Deep Learning Features for End-to-End Delayed Cerebral Ischemia Prediction on Noncontrast CT.AJNR. American journal of neuroradiology · 2024Article
- Stability and consistency of symptom clusters in younger versus older patients receiving chemotherapy.BMC geriatrics · 2024Article
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11 authors.
Funding
Abstract
backgroundOur study aimed to explore the potential of radiomics features derived from CT images in predicting the prognosis and response to adjuvant chemotherapy (ACT) in patients with Stage II colorectal cancer (CRC).
methodsA total of 478 patients with confirmed stage II CRC, with 313 from Shanghai (Training set) and 165 from Beijing (Validation set) were enrolled. Optimized features were selected using GridSearchCV and Iterative Feature Elimination (IFE) algorithm. Subsequently, we developed an ensemble random forest classifier to predict the probability of disease relapse.We evaluated the performance of the model using the concordance index (C-index), precision-recall curves, and area under the precision-recall curves (AUC
resultsA radiomic model (namely the RF5 model) consisting of four radiomics features and T stage were developed. The RF5 model performed better than simple radiomics features or T stage alone, with higher C-index and AUC
conclusionThe radiomic model can serve as a reliable tool for assessing prognosis and identifying the optimal candidates for ACT in Stage II CRC patients.
trial registrationRetrospectively registered.
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