ArticleFrontiers in public health2021
Predicting Colorectal Cancer Recurrence and Patient Survival Using Supervised Machine Learning Approach: A South African Population-Based Study.
Article in Frontiers in public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 23 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Development of a machine learning-based model to predict urethral recurrence following radical cystectomy: a multicentre retrospective study and updated meta-analysis.Scientific reports · 2025Pooled it
- Harnessing data science to control non-communicable diseases in Africa: a systematic review and gap analysis.Communications medicine · 2026Article
- Machine learning prediction of thyroid cancer recurrence for early screening and clinical decision pathways: a retrospective cohort study.Discover oncology · 2026Article
- Exploring Machine Learning Approaches for Decision Support in Neoadjuvant Therapy of Locally Advanced Rectal Cancer.Oncology research · 2026Article
- Analysis of Risk Factors and Assessment of miRNA-122 as a Novel Biomarker for Colon Cancer in Egyptian Patients.Asian Pacific journal of cancer prevention : APJCP · 2026Article
- Machine learning-based survival prediction in colorectal cancer combining clinical and biological features.Oncotarget · 2025Article
- Article
- Development and validation of a machine learning-based survival prediction model for Asian glioblastoma patients using the SEER database and Chinese data.Scientific reports · 2025Article
- Review
- Random forest with preoperative core biopsy categories: a novel method for refining ultrasonic Breast Imaging Reporting and Data System evaluation.Quantitative imaging in medicine and surgery · 2025Article
- Prognostic correlation analysis of colorectal cancer patients based on monocyte to lymphocyte ratio and folate receptor-positive circulating tumor cells and construction of a machine learning survival prediction models.Frontiers in oncology · 2025Article
- Article
- From Crypts to Cancer: A Holistic Perspective on Colorectal Carcinogenesis and Therapeutic Strategies.International journal of molecular sciences · 2024Review
- Artificial Intelligence Applications in the Treatment of Colorectal Cancer: A Narrative Review.Clinical Medicine Insights. Oncology · 2024Review
- Development of Prediction Model for 5-year Survival of Colorectal Cancer.Cancer informatics · 2024Article
- Impact of artificial intelligence and digital technology-based diagnostic tools for communicable and non-communicable diseases in Africa.African journal of laboratory medicine · 2024Review
- Machine learning-based classifiers to predict metastasis in colorectal cancer patients.Frontiers in artificial intelligence · 2024Article
- Classification and Diagnostic Prediction of Colorectal Cancer Mortality Based on Machine Learning Algorithms: A Multicenter National Study.Asian Pacific journal of cancer prevention : APJCP · 2024Article
- Ultrasound-based prediction of preoperative core biopsy categories in solid breast tumor using machine learning.Quantitative imaging in medicine and surgery · 2023Article
- Predicting metastasis in gastric cancer patients: machine learning-based approaches.Scientific reports · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
Abstract
PubMed holds no abstract for this paper.
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What OpenQuestion holds
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.