Evidence map›Paper›PMID 36749494›Full record

ReviewCurrent oncology reports2023

The Evidence for Using Artificial Intelligence to Enhance Prostate Cancer MR Imaging.

Rodrigo Canellas, Marc D Kohli, Antonio C Westphalen

Abstract readReview
PubMed Publisher
In one paragraph

Review in Current oncology reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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.

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

2 citing papers in PubMed.

  1. Review
  2. Review
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

3 authors.

Rodrigo CanellasDepartment of Radiology, University of Washington, 1959 NE Pacific St., 2nd Floor, Seattle, WA, 98195, USA.ORCID 0000-0002-1984-8867
Marc D KohliClinical Informatics, Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, 94143, USA.
Antonio C WestphalenDepartment of Radiology, University of Washington, 1959 NE Pacific St., 2nd Floor, Seattle, WA, 98195, USA. acwestph@uw.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purpose of reviewThe purpose of this review is to summarize the current status of artificial intelligence applied to prostate cancer MR imaging. RECENT

findingsArtificial intelligence has been applied to prostate cancer MR imaging to improve its diagnostic accuracy and reproducibility of interpretation. Multiple models have been tested for gland segmentation and volume calculation, automated lesion detection, localization, and characterization, as well as prediction of tumor aggressiveness and tumor recurrence. Studies show, for example, that very robust automated gland segmentation and volume calculations can be achieved and that lesions can be detected and accurately characterized. Although results are promising, we should view these with caution. Most studies included a small sample of patients from a single institution and most models did not undergo proper external validation. More research is needed with larger and well-design studies for the development of reliable artificial intelligence tools.

Indexed as

Artificial IntelligenceProstatic NeoplasmsHumansMagnetic Resonance ImagingMaleNeoplasm Recurrence, LocalReproducibility of ResultsArtificial intelligenceDeep learningMRIProstate cancerRadiomics

Identifiers

PMID36749494

What OpenQuestion holds

Textmetadata
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.