ReviewExploration of targeted anti-tumor therapy2023
Artificial intelligence and radiomics in magnetic resonance imaging of rectal cancer: a review.
Review in Exploration of targeted anti-tumor therapy, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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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
13 citing papers in PubMed, 16 citations in OpenAlex.
- MRI assessment of extramural venous invasion in rectal cancer: a practical pictorial review.Abdominal radiology (New York) · 2026Review
- Lymphocytic Reaction Combined with MRI Deep Learning Radiomics in Risk Stratification of Distant Metastases in Rectal Cancer.Cancers · 2026Article
- Significant Correlation of Dynamic Pan-Immune Inflammation Value and Magnetic Resonance Imaging Response in Patients with Locally Advanced Rectal Cancer Treated with Total Neoadjuvant Therapy.Current oncology (Toronto, Ont.) · 2026Article
- MRI's evolving role in rectal cancer in the era of personalized medicine.Abdominal radiology (New York) · 2026Review
- Clinical predictors of tocilizumab response in moderate-to-severe thyroid eye disease: a large single-center retrospective cohort study.Frontiers in endocrinology · 2026Article
- Artificial intelligence in cancer: applications, challenges, and future perspectives.Molecular cancer · 2025Review
- MRI-based radiomics for preoperative T-staging of rectal cancer: a retrospective analysis.International journal of colorectal disease · 2025Article
- The Role of Predictive and Prognostic MRI-Based Biomarkers in the Era of Total Neoadjuvant Treatment in Rectal Cancer.Cancers · 2024Review
- Different radiomics annotation methods comparison in rectal cancer characterisation and prognosis prediction: a two-centre study.Insights into imaging · 2024Article
- Future direction of total neoadjuvant therapy for locally advanced rectal cancer.Nature reviews. Gastroenterology & hepatology · 2024Review
- Machine Learning and Radiomics Analysis for Tumor Budding Prediction in Colorectal Liver Metastases Magnetic Resonance Imaging Assessment.Diagnostics (Basel, Switzerland) · 2024Article
- The Multipurpose Usage of Diffusion-Weighted MRI in Rectal Cancer.Medicina (Kaunas, Lithuania) · 2023Article
- Diffusion-weighted imaging increases the diagnostic performance of magnetic resonance imaging in assessing rectal cancer T stage and reduces intra- and interobserver variability.Annals of gastroenterologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Rectal cancer (RC) is one of the most common tumours worldwide in both males and females, with significant morbidity and mortality rates, and it accounts for approximately one-third of colorectal cancers (CRCs). Magnetic resonance imaging (MRI) has been demonstrated to be accurate in evaluating the tumour location and stage, mucin content, invasion depth, lymph node (LN) metastasis, extramural vascular invasion (EMVI), and involvement of the mesorectal fascia (MRF). However, these features alone remain insufficient to precisely guide treatment decisions. Therefore, new imaging biomarkers are necessary to define tumour characteristics for staging and restaging patients with RC. During the last decades, RC evaluation via MRI-based radiomics and artificial intelligence (AI) tools has been a research hotspot. The aim of this review was to summarise the achievement of MRI-based radiomics and AI for the evaluation of staging, response to therapy, genotyping, prediction of high-risk factors, and prognosis in the field of RC. Moreover, future challenges and limitations of these tools that need to be solved to favour the transition from academic research to the clinical setting will be discussed.
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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.