ArticleAnnals of nuclear medicine2021
Preoperative prediction of regional lymph node metastasis of colorectal cancer based on
Article in Annals of nuclear medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it, 38 citations in OpenAlex.
- Diagnostic role of F-18 FDG PET/CT in determining preoperative Lymph node status of patients with rectal cancer: a meta-analysis.Abdominal radiology (New York) · 2024Pooled it
- Machine learning and deep learning models for predicting colorectal cancer metastases: A comprehensive review.European journal of radiology open · 2026Review
- A Novel Ensemble Framework for Comprehensive Early-Stage Colorectal Cancer Diagnosis, Prognosis, and Treatment: Integration of Gastroenterology-Specific Transformer Language Models and Multiple Decision Trees.Journal of clinical medicine · 2025Article
- A Narrative Review on the Role of Artificial Intelligence (AI) in Colorectal Cancer Management.Cureus · 2025Review
- Article
- Organotropic metastasis in colorectal cancer: integrating molecular pathways with therapeutic opportunities.Frontiers in immunology · 2025Review
- Machine learning prediction model for lateral lymph node metastasis in rectal cancer.Frontiers in gastroenterology (Lausanne, Switzerland) · 2025Article
- F-18 FDG PET/CT based Preoperative Machine Learning Prediction Models for Evaluating Regional Lymph Node Metastasis Status of Patients with Colon Cancer.Asian Pacific journal of cancer prevention : APJCP · 2025Article
- Can we skip invasive biopsy of sentinel lymph nodes? A preliminary investigation to predict sentinel lymph node status using PET/CT-based radiomics.BMC cancer · 2024Article
- More than meets the eye: 2-[European journal of nuclear medicine and molecular imaging · 2024Article
- Clinical application ofJapanese journal of radiology · 2024Review
- Diagnostic value of a radiomics model based on CT and MRI for prediction of lateral lymph node metastasis of rectal cancer.Updates in surgery · 2023Article
- Using machine learning algorithms to predict 28-day mortality in critically ill elderly patients with colorectal cancer.The Journal of international medical research · 2023Article
- Contribution of Metabolic Parameters and Pericolic Fat Stranding on Preoperative 18F-FDG PET/CT in Predicting Post-operative Histopathology and Outcome in Colorectal Cancer.Nuclear medicine and molecular imaging · 2023Article
- Radiomics in colorectal cancer patients.World journal of gastroenterology · 2023Review
- Prediction of Microsatellite Instability in Colorectal Cancer Using a Machine Learning Model Based on PET/CT Radiomics.Yonsei medical journal · 2023Article
- A machine-learning-based combination of criteria to detect bladder cancer lymph node metastasis on [European radiology · 2023Article
- Preoperative prediction of lymphovascular invasion of colorectal cancer by radiomics based on 18F-FDG PET-CT and clinical factors.Frontiers in radiology · 2023Article
- Machine learning in metastatic cancer research: Potentials, possibilities, and prospects.Computational and structural biotechnology journal · 2023Review
- Role of artificial intelligence in risk prediction, prognostication, and therapy response assessment in colorectal cancer: current state and future directions.Frontiers in oncology · 2023Review
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo establish and validate a regional lymph node (LN) metastasis prediction model of colorectal cancer (CRC) based on
methodsA total of 199 colorectal cancer patients underwent pre-therapy diagnostic
resultsThe CRC patients were divided into a training set (n = 144) and a test set (n = 55). Two radiomic features were selected to build the Rad-score. Five machine-learning algorithms including logistic regression, support vector machine (SVM), random forest, neural network and eXtreme gradient boosting (XGBoost) were used to established models. Among the five machine-learning models, logistic regression (AUC 0.866, 95% CI 0.808-0.925) and XGBoost (AUC 0.903, 95% CI 0.855-0.951) models performed the best. In the training set, the AUC of these two models were significantly higher than that of the LN metastasis status reported by
conclusionIn this study, five machine-learning models for preoperative prediction of regional LN metastasis of CRC based on
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Registered trials
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.