Evidence map›Paper›PMID 30820462›Full record

ReviewNPJ precision oncology2019

Artificial intelligence for precision oncology: beyond patient stratification.

Francisco Azuaje

Registry-linked trialAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in NPJ precision oncology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06108310 (ATTRACT - ArTificial inTelligence-based RAdiogenomics in Colon Tumors), which is not on this map. Cited by 90 papers.

0numbers the graph read from it
0cells of the map it votes in
90citing papers in PubMed
–field-weighted citation impact
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.

NCT06108310 unknown statusnot on this mapstarted 2021, after this paper: background citation

ATTRACT - ArTificial inTelligence-based RAdiogenomics in Colon Tumors

TypeobservationalSponsorUniversity of Roma La SapienzaRan2021 to 2025Enrolled500ConditionsColon Cancer Stage II, Colon Cancer Stage III
3 · Its place in the literature

Who cites it

90 citing papers in PubMed.

  1. Review
  2. Review
  3. Integrating multi-omics data for next-generation cancer research and precision medicine.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026
    Review
  4. Article
  5. Review
  6. Review
  7. Review
  8. Review
  9. Review
  10. Review
  11. Article
  12. Review
  13. Article
  14. Review
  15. Review
  16. Review
  17. Review
  18. Article
  19. Review
  20. Article

30 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

1 author.

Francisco Azuaje1Bioinformatics and Modelling Research Group, Department of Oncology, Luxembourg Institute of Health (LIH), L-1445 Strassen, Luxembourg.ORCID 0000-0002-9207-9072

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The data-driven identification of disease states and treatment options is a crucial challenge for precision oncology. Artificial intelligence (AI) offers unique opportunities for enhancing such predictive capabilities in the lab and the clinic. AI, including its best-known branch of research, machine learning, has significant potential to enable precision oncology well beyond relatively well-known pattern recognition applications, such as the supervised classification of single-source omics or imaging datasets. This perspective highlights key advances and challenges in that direction. Furthermore, it argues that AI's scope and depth of research need to be expanded to achieve ground-breaking progress in precision oncology.

Identifiers

PMID30820462
PMCPMC6389974

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.