ReviewFrontiers in oncology2023
Role of artificial intelligence in risk prediction, prognostication, and therapy response assessment in colorectal cancer: current state and future directions.
Review in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Use of artificial intelligence for the prediction of lymph node metastases in early-stage colorectal cancer: systematic review.BJS open · 2024Pooled it
- Prognostic value of the tumor-to-liver density ratio in patients with metastatic colorectal cancer treated with bevacizumab-based chemotherapy. A post-hoc study of the STIC-AVASTIN trial.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Trial
- The Role of Monoclonal Antibody Targeted Therapy (Panitumumab) in the Treatment of Metastatic Colorectal Cancer-A Narrative Review of Current Evidence and Emerging Therapeutic Strategies.International journal of molecular sciences · 2026Review
- Chinese expert concern and consensus on applications of artificial intelligence in clinical cancer imaging.Insights into imaging · 2026Article
- Review
- A systematic review of artificial intelligence and machine learning for gut microbiome-based CRC screening.Journal of gastrointestinal oncology · 2026Review
- Comparison of artificial intelligence and multidisciplinary team recommendations in the management of colorectal cancer liver metastases.Scientific reports · 2026Article
- Preoperative prediction of lymphatic metastasis in rectal cancer using a fusion model based on multiparameter magnetic resonance imaging: a retrospective validation study.Frontiers in oncology · 2026Article
- 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
- No operation after short-course radiotherapy followed by consolidation chemotherapy in locally advanced rectal cancer (NOAHS-ARC): study protocol for a prospective, phase II trial.International journal of colorectal disease · 2025Article
- Population-based colorectal cancer risk prediction using a SHAP-enhanced LightGBM model.Frontiers in oncology · 2025Article
- A one health perspective on multidrug-resistant bacterial infections: integrated approaches for surveillance, policy and innovation.Frontiers in cellular and infection microbiology · 2025Review
- Evolving and Novel Applications of Artificial Intelligence in Abdominal Imaging.Tomography (Ann Arbor, Mich.) · 2024Review
- Harnessing Artificial Intelligence for the Detection and Management of Colorectal Cancer Treatment.Cancer prevention research (Philadelphia, Pa.) · 2024Review
- Future direction of total neoadjuvant therapy for locally advanced rectal cancer.Nature reviews. Gastroenterology & hepatology · 2024Review
- Artificial Intelligence, the Digital Surgeon: Unravelling Its Emerging Footprint in Healthcare - The Narrative Review.Journal of multidisciplinary healthcare · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial Intelligence (AI) is a branch of computer science that utilizes optimization, probabilistic and statistical approaches to analyze and make predictions based on a vast amount of data. In recent years, AI has revolutionized the field of oncology and spearheaded novel approaches in the management of various cancers, including colorectal cancer (CRC). Notably, the applications of AI to diagnose, prognosticate, and predict response to therapy in CRC, is gaining traction and proving to be promising. There have also been several advancements in AI technologies to help predict metastases in CRC and in Computer-Aided Detection (CAD) Systems to improve miss rates for colorectal neoplasia. This article provides a comprehensive review of the role of AI in predicting risk, prognosis, and response to therapies among patients with CRC.
Indexed as
Identifiers
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.