Evidence map›Paper›PMID 35125822›Full record

ReviewWorld journal of gastroenterology2022

Artificial intelligence in the diagnosis and management of colorectal cancer liver metastases.

Gianluca Rompianesi, Francesca Pegoraro, Carlo Dl Ceresa, Roberto Montalti, Roberto Ivan Troisi

Registry-linked trialOpen access · hybridAbstract readReview
In one paragraph

Review in World journal of gastroenterology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07716670 (Development and Prospective Validation of an AI-Based Diagnostic Model for Hepato-Pancreato-Biliary Diseases), which is not on this map. Cited by 73 papers.

0numbers the graph read from it
0cells of the map it votes in
73citing papers in PubMed
9.4field-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.

NCT07716670 not yet recruitingnot on this mapstarted 2026, after this paper: background citation

Development and Prospective Validation of an AI-Based Diagnostic Model for Hepato-Pancreato-Biliary Diseases

Typeobservational_patient_registrySponsorSecond Affiliated Hospital, Zhejiang University, School of MedicineRan2026 to 2026Enrolled400ConditionsHepatic Disease, Biliary Disease, Pancreas Disease, Artificial Intelligence (AI) in DiagnosisArmsAI Integration type 1, AI Integration type 2
3 · Its place in the literature

Who cites it

73 citing papers in PubMed, 126 citations in OpenAlex.

  1. Article
  2. Article
  3. Liver metastases beyond classical imaging findings.Abdominal radiology (New York) · 2026
    Review
  4. Review
  5. Article
  6. Review
  7. Review
  8. Review
  9. Article
  10. Evaluation of the performance of radiologists assisted by AI in detecting colorectal liver metastases on contrast-enhanced CT.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026
    Article
  11. Article
  12. Article
  13. Review
  14. Article
  15. Review
  16. Article
  17. Review
  18. Review
  19. Review
  20. Review

13 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.

Gianluca RompianesiDivision of Hepato-Bilio-Pancreatic, Minimally Invasive and Robotic Surgery, Department of Clinical Medicine and Surgery, Federico II University Hospital, Naples 80125, Italy. gianlucarompianesi@gmail.com.
Francesca PegoraroDivision of Hepato-Bilio-Pancreatic, Minimally Invasive and Robotic Surgery, Department of Clinical Medicine and Surgery, Federico II University Hospital, Naples 80125, Italy.
Carlo Dl CeresaDepartment of Hepato-Pancreato-Biliary Surgery, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9ES, United Kingdom.
Roberto MontaltiDivision of Hepato-Bilio-Pancreatic, Minimally Invasive and Robotic Surgery, Department of Public Health, Federico II University Hospital, Naples 80125, Italy.
Roberto Ivan TroisiDivision of Hepato-Bilio-Pancreatic, Minimally Invasive and Robotic Surgery, Department of Clinical Medicine and Surgery, Federico II University Hospital, Naples 80125, Italy.
Federico II University Hospital · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Colorectal cancer (CRC) is the third most common malignancy worldwide, with approximately 50% of patients developing colorectal cancer liver metastasis (CRLM) during the follow-up period. Management of CRLM is best achieved

Indexed as

Colorectal NeoplasmsLiver NeoplasmsArtificial IntelligenceHumansProspective StudiesQuality of LifeArtificial intelligenceColorectal cancerDeep learningLiver metastasesMachine learningNeural networksRadiomics

Identifiers

PMID35125822
PMCPMC8793013
OpenAlexW4206538733

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

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.