Evidence map›Paper›PMID 33922402›Full record

ReviewCurrent oncology (Toronto, Ont.)2021

Artificial Intelligence in Colorectal Cancer Screening, Diagnosis and Treatment. A New Era.

Athanasia Mitsala, Christos Tsalikidis, Michail Pitiakoudis, Constantinos Simopoulos, Alexandra K Tsaroucha

Open access · goldAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Current oncology (Toronto, Ont.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 127 papers, 7 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
127citing papers in PubMed, 7 pooled it
14.5field-weighted citation impact, top 1% of its field
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

127 citing papers in PubMed, 7 syntheses or guidelines pooled it, 257 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Computer-aided diagnosis for colorectal polyp in comparison with endoscopists: Systematic review and meta-analysis.Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society · 2025
    Pooled it
  4. Pooled it
  5. Pooled it
  6. Pooled it
  7. Pooled it
  8. Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. A deep learning-based automated pipeline for colorectal cancer detection in contrast-enhanced CT images.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2026
    Article
  14. Review
  15. Review
  16. Article
  17. From mono- to multi-cellularFrontiers in cell and developmental biology · 2026
    Review
  18. Review
  19. Review
  20. Article

67 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Athanasia MitsalaSecond Department of Surgery, University General Hospital of Alexandroupolis, Democritus University of Thrace Medical School, Dragana, 68100 Alexandroupolis, Greece.
Christos TsalikidisSecond Department of Surgery, University General Hospital of Alexandroupolis, Democritus University of Thrace Medical School, Dragana, 68100 Alexandroupolis, Greece.
Michail PitiakoudisSecond Department of Surgery, University General Hospital of Alexandroupolis, Democritus University of Thrace Medical School, Dragana, 68100 Alexandroupolis, Greece.
Constantinos SimopoulosSecond Department of Surgery, University General Hospital of Alexandroupolis, Democritus University of Thrace Medical School, Dragana, 68100 Alexandroupolis, Greece.
Alexandra K TsarouchaLaboratory of Experimental Surgery & Surgical Research, Democritus University of Thrace Medical School, Dragana, 68100 Alexandroupolis, Greece.
Democritus University of Thrace · GR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The development of artificial intelligence (AI) algorithms has permeated the medical field with great success. The widespread use of AI technology in diagnosing and treating several types of cancer, especially colorectal cancer (CRC), is now attracting substantial attention. CRC, which represents the third most commonly diagnosed malignancy in both men and women, is considered a leading cause of cancer-related deaths globally. Our review herein aims to provide in-depth knowledge and analysis of the AI applications in CRC screening, diagnosis, and treatment based on current literature. We also explore the role of recent advances in AI systems regarding medical diagnosis and therapy, with several promising results. CRC is a highly preventable disease, and AI-assisted techniques in routine screening represent a pivotal step in declining incidence rates of this malignancy. So far, computer-aided detection and characterization systems have been developed to increase the detection rate of adenomas. Furthermore, CRC treatment enters a new era with robotic surgery and novel computer-assisted drug delivery techniques. At the same time, healthcare is rapidly moving toward precision or personalized medicine. Machine learning models have the potential to contribute to individual-based cancer care and transform the future of medicine.

Indexed as

AdenomaColorectal NeoplasmsArtificial IntelligenceColonoscopyEarly Detection of CancerFemaleHumansMaleartificial intelligencecolonoscopycolorectal cancercomputer-aided detectioncomputer-aided diagnosisprecision oncologyrobotic-assisted surgeryscreeningtherapy

Identifiers

PMID33922402
PMCPMC8161764
OpenAlexW3154734525

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.