ReviewWorld journal of gastroenterology2021
Emerging applications of radiomics in rectal cancer: State of the art and future perspectives.
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 19 papers.
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
19 citing papers in PubMed.
- Radiomic analysis for predicting prognosis of colorectal cancer from preoperativeJournal of translational medicine · 2022Trial
- Radiomics' Role in Predicting Distant Metastases, Recurrence and Survival Outcome in Rectal Cancer: A Systematic Review.Cancers · 2026Review
- Interpretable MRI Subregional Radiomics-Deep Learning Model for Preoperative Lymphovascular Invasion Prediction in Rectal Cancer: A Dual-Center Study.Journal of imaging informatics in medicine · 2026Article
- Article
- Research Progress of MRI-based Radiomics in Rectal CancerCurrent medical imaging · 2026Review
- Preoperative magnetic resonance imaging-based radiomics nomogram model for predicting postoperative anastomotic leakage in colorectal cancer.Annals of coloproctology · 2025Article
- Development of a CT radiomics model for detection of bladder invasion by colorectal carcinoma.Scientific reports · 2025Article
- Can MRI radiomics distinguish residual adenocarcinoma from acellular mucin in treated rectal cancer?European journal of radiology · 2025Article
- Predictive value of magnetic resonance imaging parameters combined with tumor markers for rectal cancer recurrence risk after surgery.World journal of gastrointestinal surgery · 2025Article
- A Machine Learning Model Based on MRI Radiomics to Predict Response to Chemoradiation Among Patients with Rectal Cancer.Life (Basel, Switzerland) · 2024Article
- Radiomics in rectal cancer: current status of use and advances in research.Frontiers in oncology · 2024Review
- Development and validation of a competitive risk model in patients with rectal cancer: based on SEER database.European journal of medical research · 2023Article
- Biparametric magnetic resonance imaging-based radiomics features for prediction of lymphovascular invasion in rectal cancer.BMC cancer · 2023Article
- Region-specific deep learning models for accurate segmentation of rectal structures on post-chemoradiation T2w MRI: a multi-institutional, multi-reader study.Frontiers in medicine · 2023Article
- Role of Machine Learning in Precision Oncology: Applications in Gastrointestinal Cancers.Cancers · 2022Review
- Deep learning based radiomics for gastrointestinal cancer diagnosis and treatment: A minireview.World journal of gastroenterology · 2022Review
- Endorectal ultrasound radiomics in locally advanced rectal cancer patients: despeckling and radiotherapy response prediction using machine learning.Abdominal radiology (New York) · 2022Article
- An endorectal ultrasound-based radiomics signature for preoperative prediction of lymphovascular invasion of rectal cancer.BMC medical imaging · 2022Article
- Application of Radiomics in Predicting Treatment Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer: Strategies and Challenges.Journal of oncology · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Rectal cancer (RC) is the third most commonly diagnosed cancer and has a high risk of mortality, although overall survival rates have improved. Preoperative assessments and predictions, including risk stratification, responses to therapy, long-term clinical outcomes, and gene mutation status, are crucial to guide the optimization of personalized treatment strategies. Radiomics is a novel approach that enables the evaluation of the heterogeneity and biological behavior of tumors by quantitative extraction of features from medical imaging. As these extracted features cannot be captured by visual inspection, the field holds significant promise. Recent studies have proved the rapid development of radiomics and validated its diagnostic and predictive efficacy. Nonetheless, existing radiomics research on RC is highly heterogeneous due to challenges in workflow standardization and limitations of objective cohort conditions. Here, we present a summary of existing research based on computed tomography and magnetic resonance imaging. We highlight the most salient issues in the field of radiomics and analyze the most urgent problems that require resolution. Our review provides a cutting-edge view of the use of radiomics to detect and evaluate RC, and will benefit researchers dedicated to using this state-of-the-art technology in the era of precision medicine.
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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.