ArticleCancer medicine2020
MRI-based radiomics nomogram to predict synchronous liver metastasis in primary rectal cancer patients.
Article in Cancer medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 2 of them syntheses that pooled it.
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Who cites it
35 citing papers in PubMed, 2 syntheses or guidelines pooled it, 45 citations in OpenAlex.
- 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
- Artificial intelligence in colorectal surgery: an AI-powered systematic review.Techniques in coloproctology · 2023Pooled it
- 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
- Predicting Mismatch-Repair Status in Rectal Cancer Using Multiparametric MRI-Based Radiomics Models: A Preliminary Study.BioMed research international · 2022Trial
- Radiomics' Role in Predicting Distant Metastases, Recurrence and Survival Outcome in Rectal Cancer: A Systematic Review.Cancers · 2026Review
- Prediction of colorectal cancer liver metastasis through an MRI radiomic model.Scientific reports · 2026Article
- Artificial intelligence and computational prediction models for risk stratification, treatment response, and outcomes in colorectal cancer: a narrative review.Frontiers in oncology · 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
- 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
- Organotropic metastasis in colorectal cancer: integrating molecular pathways with therapeutic opportunities.Frontiers in immunology · 2025Review
- Radiomics in rectal cancer: current status of use and advances in research.Frontiers in oncology · 2024Review
- Colorectal Cancer: Current Updates and Future Perspectives.Journal of clinical medicine · 2023Review
- Radiomics and machine learning analysis by computed tomography and magnetic resonance imaging in colorectal liver metastases prognostic assessment.La Radiologia medica · 2023Article
- Artificial Intelligence to Early Predict Liver Metastases in Patients with Colorectal Cancer: Current Status and Future Prospectives.Life (Basel, Switzerland) · 2023Review
- Focal Lesions of the Liver and Radiomics: What Do We Know?Diagnostics (Basel, Switzerland) · 2023Review
- Radiomics in colorectal cancer patients.World journal of gastroenterology · 2023Review
- Colorectal liver metastases patients prognostic assessment: prospects and limits of radiomics and radiogenomics.Infectious agents and cancer · 2023Review
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
At the time of diagnosis, approximately 15%-20% of patients with rectal cancer (RC) presented synchronous liver metastasis (SLM), which is the most common cause of death in patients with RC. Therefore, preoperative, noninvasive, and accurate prediction of SLM is crucial for personalized treatment strategies. Recently, radiomics has been considered as an advanced image analysis method to evaluate the neoplastic heterogeneity with respect to diagnosis of the tumor and prediction of prognosis. In this study, a total of 1409 radiomics features were extracted for each volume of interest (VOI) from high-resolution T2WI images of the primary RC. Subsequently, five optimal radiomics features were selected based on the training set using the least absolute shrinkage and selection operator (LASSO) method to construct the radiomics signature. In addition, radiomics signature combined with carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA19-9) was included in the multifactor logistic regression to construct the nomogram model. It showed an optimal predictive performance in the validation set as compared to that in the radiomics model. The favorable calibration of the radiomics nomogram showed a nonsignificant Hosmer-Lemeshow test statistic (P > .05). The decision curve analysis (DCA) showed that the radiomics nomogram is clinically superior to the radiomics model. Therefore, the nomogram amalgamating the radiomics signature and clinical risk factors serve as an effective quantitative approach to predict the SLM of primary RC.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.