ArticleEuropean radiology2019
MRI radiomics analysis for predicting preoperative synchronous distant metastasis in patients with rectal cancer.
Article in European radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers.
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Who cites it
61 citing papers in PubMed.
- Development and validation of a radiomics model of magnetic resonance for predicting liver metastasis in resectable pancreatic ductal adenocarcinoma patients.Radiation oncology (London, England) · 2023Trial
- Radiomics model based on multi-sequence MR images for predicting preoperative immunoscore in rectal cancer.La Radiologia medica · 2022Trial
- Radiomics' Role in Predicting Distant Metastases, Recurrence and Survival Outcome in Rectal Cancer: A Systematic Review.Cancers · 2026Review
- Multiparametric MRI radiomics nomogram predicts synchronous distant metastasis in rectal cancer.Scientific reports · 2026Article
- Research Progress of MRI-based Radiomics in Rectal CancerCurrent medical imaging · 2026Review
- A nomogram based on dual-layer spectral detector CT-derived 40KeV virtual monoenergetic images for preoperative prediction of simultaneous distant metastasis in colorectal cancer.BMC medical imaging · 2025Article
- Deep learning models based on multiparametric magnetic resonance imaging and clinical parameters for identifying synchronous liver metastases from rectal cancer.BMC medical imaging · 2025Article
- Organotropic metastasis in colorectal cancer: integrating molecular pathways with therapeutic opportunities.Frontiers in immunology · 2025Review
- Prediction of KRAS gene mutations in colorectal cancer using a CT-based radiomic model.Frontiers in medicine · 2025Article
- Diagnostic accuracy of artificial intelligence based on imaging data for predicting distant metastasis of colorectal cancer: a systematic review and meta-analysis.Frontiers in oncology · 2025Article
- MRI T2WI-based radiomics combined with KRAS gene mutation constructed models for predicting liver metastasis in rectal cancer.BMC medical imaging · 2024Article
- Imaging at the nexus: how state of the art imaging techniques can enhance our understanding of cancer and fibrosis.Journal of translational medicine · 2024Review
- MRI- and DWI-Based Radiomics Features for Preoperatively Predicting Meningioma Sinus Invasion.Journal of imaging informatics in medicine · 2024Article
- Development and validation of radiomics nomogram for metastatic status of epithelial ovarian cancer.Scientific reports · 2024Article
- Radiomics in rectal cancer: current status of use and advances in research.Frontiers in oncology · 2024Review
- Evaluation of the neoadjuvant chemotherapy response in osteosarcoma using the MRI DWI-based machine learning radiomics nomogram.Frontiers in oncology · 2024Article
- Construction of prediction model for KRAS mutation status of colorectal cancer based on CT radiomics.Japanese journal of radiology · 2023Article
- A novel radiomics based on multi-parametric magnetic resonance imaging for predicting Ki-67 expression in rectal cancer: a multicenter study.BMC medical imaging · 2023Article
- Development and external validation of a multiparametric MRI-based radiomics model for preoperative prediction of microsatellite instability status in rectal cancer: a retrospective multicenter study.European radiology · 2023Article
- Predicting the risk of distant metastasis in patients with locally advanced rectal cancer using model based on pre-treatment T2WI-based radiomic features plus postoperative pathological stage.Frontiers in oncology · 2023Article
1 more citing papers are in PubMed but not listed here.
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11 authors.
Funding
Abstract
objectivesTo investigate the value of MRI radiomics based on T2-weighted (T2W) images in predicting preoperative synchronous distant metastasis (SDM) in patients with rectal cancer.
methodsThis retrospective study enrolled 177 patients with histopathology-confirmed rectal adenocarcinoma (123 patients in the training cohort and 54 in the validation cohort). A total of 385 radiomics features were extracted from pretreatment T2W images. Five steps, including univariate statistical tests and a random forest algorithm, were performed to select the best preforming features for predicting SDM. Multivariate logistic regression analysis was conducted to build the clinical and clinical-radiomics combined models in the training cohort. The predictive performance was validated by receiver operating characteristics curve (ROC) analysis and clinical utility implementing a nomogram and decision curve analysis.
resultsFifty-nine patients (33.3%) were confirmed to have SDM. Six radiomics features and four clinical characteristics were selected for predicting SDM. The clinical-radiomics combined model performed better than the clinical model in both the training and validation datasets. A threshold of 0.44 yielded an area under the ROC (AUC) value of 0.827 (95% confidence interval (CI), 0.6963-0.9580), a sensitivity of 72.2%, a specificity of 94.4%, and an accuracy of 87.0% in the validation cohort for the combined model. A clinical-radiomics nomogram and decision curve analysis confirmed the clinical utility of the combined model.
conclusionsOur proposed clinical-radiomics combined model could be utilized as a noninvasive biomarker for identifying patients at high risk of SDM, which could aid in tailoring treatment strategies. KEY POINTS: • T2WI-based radiomics analysis helps predict synchronous distant metastasis (SDM) of rectal cancer. • The clinical-radiomics combined model could be utilized as a noninvasive biomarker for predicting SDM. • Personalized treatment can be carried out with greater confidence based on the risk stratification for SDM in rectal cancer.
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