ArticleAbdominal radiology (New York)2023
Radiomics based on preoperative rectal cancer MRI to predict the metachronous liver metastasis.
Article in Abdominal radiology (New York), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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15 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 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
- A Vision Transformer- and Radiomics-based Model for Predicting Liver Metastasis-Free Survival in Patients with Rectal Cancer.Radiology. Imaging cancer · 2026Article
- Prediction of lymphovascular invasion in rectal cancer based on multimodal magnetic resonance imaging radiomics model.World journal of gastrointestinal surgery · 2026Article
- Prediction of colorectal cancer liver metastasis through an MRI radiomic model.Scientific reports · 2026Article
- Interpretable habitat radiomics model based on multi-sequence MRI for risk prediction of metachronous liver metastasis in rectal cancer: a multicenter study.Japanese journal of radiology · 2026Article
- Multiparametric MRI radiomics nomogram predicts synchronous distant metastasis in rectal cancer.Scientific reports · 2026Article
- Comparison of Radiomic Features from Different MRI Sequences for Predicting Synchronous Liver Metastases after Rectal CancerCurrent medical imaging · 2026Article
- Machine learning-based radiomics models for the prediction of metachronous liver metastases in patients with colorectal cancer: A multimodal study.Oncology letters · 2025Article
- Research on the developments of artificial intelligence in radiomics for oncology over the past decade: a bibliometric and visualized analysis.Discover oncology · 2025Article
- Multiparameter magnetic resonance imaging-based radiomics model for the prediction of rectal cancer metachronous liver metastasis.World journal of gastrointestinal oncology · 2025Article
- Combining radiomics and deep learning to predict liver metastasis of gastric cancer on CT image.Frontiers in oncology · 2025Article
- A Joint Classification Method for COVID-19 Lesions Based on Deep Learning and Radiomics.Tomography (Ann Arbor, Mich.) · 2024Article
- Machine Learning and Radiomics Analysis for Tumor Budding Prediction in Colorectal Liver Metastases Magnetic Resonance Imaging Assessment.Diagnostics (Basel, Switzerland) · 2024Article
- A nomogram prediction model based on clinicopathological combined radiological features for metachronous liver metastasis of colorectal cancer.Journal of Cancer · 2024Article
- Leveraging radiomics and AI for precision diagnosis and prognostication of liver malignancies.Frontiers in oncology · 2024Review
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeAt present, there are few effective method to predict metachronous liver metastasis (MLM) from rectal cancer. We aim to investigate the efficacy of radiomics based on multiparametric MRI of first diagnosed rectal cancer in predicting MLM from rectal cancer.
methodsFrom 301 consecutive histopathologically confirmed rectal cancer patients, 130 patients who have no distant metastasis detected at the time of diagnosis were enrolled and divided into MLM group (n = 49) and non-MLM group (n = 81) according to whether liver metastasis be detected later than 6 month after the first diagnosis of rectal cancer within 3 years' follow-up. The 130 patients were divided into a training set (n = 91) and a testing set (n = 39) at a ratio of 7:3 by stratified sampling using SPSS 24.0 software. The DWI model, HD T
resultsThe AUC of the DWI + HD T
conclusionThe fusion model of primary rectal cancer MRI based radiomics combing clinical features can effectively predict MLM from rectal cancer, which may assist clinicians in formulating individualized monitoring and treatment plans.
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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.