ReviewWorld journal of gastroenterology2021
Magnetic resonance imaging-based artificial intelligence model in rectal cancer.
Review in World journal of gastroenterology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Precision medicine exploration of postpartum rectus abdominis muscle separation: from basic research to clinical practice.BMC surgery · 2025Pooled it
- Artificial intelligence in magnetic resonance imaging for predicting lymph node metastasis in rectal cancer patients: a meta-analysis.European radiology · 2025Pooled it
- Fusion of MRI-radiomics and clinical features to improve pathological T-stage prediction of rectal cancer: a multicenter study.BMC cancer · 2026Article
- Role of MRI radiomics in deep learning-based prediction of intestinal diseases.International journal of colorectal disease · 2026Review
- Deep learning model for predicting lymph node metastasis around rectal cancer based on rectal tumor core area and mesangial imaging features.BMC medical imaging · 2025Article
- Review
- Deep learning on T2WI to predict the muscle-invasive bladder cancer: a multi-center clinical study.Scientific reports · 2025Article
- Application of a combined clinical prediction model based on enhanced T1-weighted image(T1WI) full volume histogram in peripheral nerve invasion (PNI) and lymphatic vessel invasion (LVI) in rectal cancer.Abdominal radiology (New York) · 2025Article
- Preoperative prediction of multiple biological characteristics in colorectal cancer using MRI and machine learning.Heliyon · 2025Article
- A pelvis MR transformer-based deep learning model for predicting lung metastases risk in patients with rectal cancer.Frontiers in oncology · 2025Article
- Role of Artificial Intelligence in MRI-Based Rectal Cancer Staging: A Systematic Review.Cureus · 2024Review
- Challenges and perspectives in use of artificial intelligence to support treatment recommendations in clinical oncology.Cancer medicine · 2024Review
- Machine Learning-Based Algorithms for Enhanced Prediction of Local Recurrence and Metastasis in Low Rectal Adenocarcinoma Using Imaging, Surgical, and Pathological Data.Diagnostics (Basel, Switzerland) · 2024Article
- Predicting the Feasibility of Curative Resection in Low Rectal Cancer: Insights from a Prospective Observational Study on Preoperative Magnetic Resonance Imaging Accuracy.Medicina (Kaunas, Lithuania) · 2024Observational
- Performance reporting design in artificial intelligence studies using image-based TNM staging and prognostic parameters in rectal cancer: a systematic review.Annals of coloproctology · 2024Review
- The Role of Artificial Intelligence in Prospective Real-Time Histological Prediction of Colorectal Lesions during Colonoscopy: A Systematic Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2023Review
- Magnetic Resonance Imaging of the Gastrointestinal Tract: Current Role, Recent Advancements and Future Prospectives.Diagnostics (Basel, Switzerland) · 2023Review
- Preoperative prediction of tumor budding in rectal cancer using multiple machine learning algorithms based on MRI T2WI radiomics.Frontiers in oncology · 2023Article
- Modern Machine Learning Practices in Colorectal Surgery: A Scoping Review.Journal of clinical medicine · 2022Article
- Risks and Benefits of Artificial Intelligence as an Adjunct in Colorectal Multidisciplinary Decision Making.ANZ journal of surgeryArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Rectal magnetic resonance imaging (MRI) is the preferred method for the diagnosis of rectal cancer as recommended by the guidelines. Rectal MRI can accurately evaluate the tumor location, tumor stage, invasion depth, extramural vascular invasion, and circumferential resection margin. We summarize the progress of research on the use of artificial intelligence (AI) in rectal cancer in recent years. AI, represented by machine learning, is being increasingly used in the medical field. The application of AI models based on high-resolution MRI in rectal cancer has been increasingly reported. In addition to staging the diagnosis and localizing radiotherapy, an increasing number of studies have reported that AI models based on high-resolution MRI can be used to predict the response to chemotherapy and prognosis of patients.
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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.