ReviewProceedings (Baylor University. Medical Center)2020
Advances in endoscopy for colorectal polyp detection and classification.
Review in Proceedings (Baylor University. Medical Center), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 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.
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Who cites it
22 citing papers in PubMed, 40 citations in OpenAlex.
- Status of robotic and manual devices for advanced colonoscopy: Trends and applications of robotics in colonoscopy.World journal of gastrointestinal pharmacology and therapeutics · 2026Review
- MAP-SCTNet: multi-scale pyramid and frequency-enhanced network for colorectal cancer histopathological image segmentation.Frontiers in medicine · 2026Article
- In Situ Photoactivated Hydrogel Adhesive Dressings for Post Colon Polypectomies (PolypCures).bioRxiv : the preprint server for biology · 2025Article
- The role of miR-145, miR-200c, and miR-21 in distinguishing between hyperplastic and adenomatous colon polyps and colorectal cancer prediction.Croatian medical journal · 2025Article
- Classification of colon polyps with malignant potential using statistical analysis of features extracted from ex vivo optical coherence tomography images.Biomedical engineering online · 2025Article
- Postcolonoscopy colorectal cancer: What we need to know in the age of screening and magnifying endoscopy techniques.World journal of gastrointestinal endoscopy · 2025Review
- Identification of colorectal malignancies enabled by phasor-based autofluorescence lifetime macroimaging and ensemble learning.Biophotonics discovery · 2025Article
- Deep learning for the detection of colon polyps with malignant potential: ex vivo classification using feature-enhanced optical coherence tomography (OCT) images.Biomedical optics express · 2025Article
- Advances in gastrointestinal endoscopy: A comprehensive review of innovations in cancer diagnosis and management.World journal of gastrointestinal endoscopy · 2025Review
- Enhancing colorectal polyp classification using gaze-based attention networks.PeerJ. Computer science · 2025Article
- Article
- Precision in detecting colon lesions: A key to effective screening policy but will it improve overall outcomes?World journal of gastrointestinal endoscopy · 2024Article
- Spatio-Temporal Feature Transformation Based Polyp Recognition for Automatic Detection: Higher Accuracy than Novice Endoscopists in Colorectal Polyp Detection and Diagnosis.Digestive diseases and sciences · 2024Article
- Computer-aided colorectal cancer diagnosis: AI-driven image segmentation and classification.PeerJ. Computer science · 2024Article
- Optimizing colorectal cancer segmentation with MobileViT-UNet and multi-criteria decision analysis.PeerJ. Computer science · 2024Article
- EBHI-Seg: A novel enteroscope biopsy histopathological hematoxylin and eosin image dataset for image segmentation tasks.Frontiers in medicine · 2023Article
- Induction of apoptosis and cell cycle arrest in colorectal cancer cells by novel anticancer metabolites of Streptomyces sp. 801.Cancer cell international · 2022Article
- Targeted Nursing Combined with Endoscopic Submucosal Injection of Carbon Nanoparticles in the Treatment of Colorectal Cancer.Evidence-based complementary and alternative medicine : eCAM · 2022Article
- Bioactive Components FromFrontiers in nutrition · 2022Article
- Analysis of dynamic molecular networks: the progression from colorectal adenoma to cancer.Journal of gastrointestinal oncology · 2021Article
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 at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
While colonoscopy is considered the gold standard for colon cancer screening, recent advancements in endoscopes have allowed for improved visualization of the colonic mucosa and improved polyp detection rates. Newer technologies also allow for assessment of structural changes for polyp discrimination and determination of histologic type. Classification of polyps prevents the need for a histopathologic report, which requires the additional time and expertise of a pathologist and adds to the overall cost. This review considered advances in endoscopic technologies reported in PubMed over the past 12 years. Technologies that allow for increased visual field of colonic mucosa and may lead to improved colon polyp detection rates include cap-assisted colonoscopy, RetroView, extra-wide-angle view colonoscope, full-spectrum endoscopy, Third Eye Retroscope, NaviAid G-EYE balloon colonoscope, EndoRings, and Endocuff. Image-enhancing methods allow for pit pattern analysis of colorectal lesions, which enables the physician to classify colorectal polyps according to certain polyp characteristics. Image-enhancing methods include chromoendoscopy, autofluorescence, and virtual chromoendoscopy, including narrow band imaging, i-SCAN, flexible spectral imaging chromoendoscopy, and STORZ professional image enhancement systems. In addition, advancements have been made in in vivo microscopic evaluation of colonic epithelium, including confocal laser endomicroscopy, endocytoscopy, optical coherence tomography, spectroscopy, and autofluorescence spectroscopy. Colon capsule endoscopy also has a role in colon polyp detection and classification. The advancements in polyp detection and classification have great promise for earlier detection and removal of advanced adenomas before they advance to colorectal cancer.
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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.