ArticleScientific reports2019
MRI-based Radiomics nomogram to detect primary rectal cancer with synchronous liver metastases.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers, 1 of them a synthesis that pooled it.
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Who cites it
59 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence for the prediction of synchronous and metachronous liver metastasis in colorectal cancer patients: a systematic review and meta-analysis.Abdominal radiology (New York) · 2026Pooled it
- Radiomics analysis for predicting malignant cerebral edema in patients undergoing endovascular treatment for acute ischemic strokeDiagnostic and interventional radiology (Ankara, Turkey) · 2023Trial
- Predicting Chronic Myocardial Ischemia Using CCTA-Based Radiomics Machine Learning Nomogram.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2022Trial
- Radiomics' Role in Predicting Distant Metastases, Recurrence and Survival Outcome in Rectal Cancer: A Systematic Review.Cancers · 2026Review
- Research Progress of MRI-based Radiomics in Rectal CancerCurrent medical imaging · 2026Review
- Comparison of Radiomic Features from Different MRI Sequences for Predicting Synchronous Liver Metastases after Rectal CancerCurrent medical imaging · 2026Article
- Development and validation of a radiopathomics model for predicting liver metastases of colorectal cancer.European radiology · 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
- Computed tomography-based deep learning radiomics model for preoperative prediction of tumor immune microenvironment in colorectal cancer.World journal of gastrointestinal oncology · 2025Article
- Predicting Hospitalization Length in Geriatric Patients Using Artificial Intelligence and Radiomics.Bioengineering (Basel, Switzerland) · 2025Article
- Complete blood counts as potential risk factors of early dissemination to liver and lungs in resected colorectal cancer: a retrospective cohort study.International journal of colorectal disease · 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
- Prediction of the prognosis of liver iron burden within two years after hematopoietic stem cell transplantation based on multimodal MRI-based radiomics model.Frontiers in medicine · 2025Article
- Organotropic metastasis in colorectal cancer: integrating molecular pathways with therapeutic opportunities.Frontiers in immunology · 2025Review
- Prediction of pathological response and lymph node metastasis after neoadjuvant therapy in rectal cancer through tumor and mesorectal MRI radiomic features.Scientific reports · 2024Article
- Review
- Machine Learning and Radiomics Analysis for Tumor Budding Prediction in Colorectal Liver Metastases Magnetic Resonance Imaging Assessment.Diagnostics (Basel, Switzerland) · 2024Article
- Radiomics in rectal cancer: current status of use and advances in research.Frontiers in oncology · 2024Review
- Artificial Intelligence in Colorectal Cancer: From Patient Screening over Tailoring Treatment Decisions to Identification of Novel Biomarkers.Digestion · 2024Review
- Artificial Intelligence to Early Predict Liver Metastases in Patients with Colorectal Cancer: Current Status and Future Prospectives.Life (Basel, Switzerland) · 2023Review
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8 authors.
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Abstract
Synchronous liver metastasis (SLM) remains a major challenge for rectal cancer. Early detection of SLM is a key factor to improve the survival rate of rectal cancer. In this radiomics study, we predicted the SLM based on the radiomics of primary rectal cancer. A total of 328 radiomics features were extracted from the T2WI images of 194 patients. The least absolute shrinkage and selection operator (LASSO) regression was used to reduce the feature dimension and to construct the radiomics signature. after LASSO, principal component analysis (PCA) was used to sort the features of the surplus characteristics, and selected the features of the total contribution of 85%. Then the prediction model was built by linear regression, and the decision curve analysis was used to judge the net benefit of LASSO and PCA. In addition, we used two independent cohorts for training (n = 135) and validation (n = 159). We found that the model based on LASSO dimensionality construction had the maximum net benefit (in the training set (AUC [95% confidence interval], 0.857 [0.787-0.912]) and in the validation set (0.834 [0.714-0.918]). The radiomics nomogram combined with clinical risk factors and LASSO features showed a good predictive performance in the training set (0.921 [0.862-0.961]) and validation set (0.912 [0.809-0.97]). Our study indicated that radiomics based on primary rectal cancer could provide a non-invasive way to predict the risk of SLM in clinical practice.
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