Evidence map›Paper›PMID 36654741›Full record

ReviewJournal of pathology informatics2023

An update on computational pathology tools for genitourinary pathology practice: A review paper from the Genitourinary Pathology Society (GUPS).

Anil V Parwani, Ankush Patel, Ming Zhou, John C Cheville, Hamid Tizhoosh, Peter Humphrey, Victor E Reuter, Lawrence D True

Erratum issuedAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Journal of pathology informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Anil V ParwaniThe Ohio State University, Columbus, Ohio, USA.
Ankush PatelThe Ohio State University, 2441 60 Ave SE, Mercer Island, Washington 98040, USA.
Ming ZhouTufts University, Medford, Massachusetts, USA.
John C ChevilleMayo Clinic, Rochester, Minnesota, USA.
Hamid TizhooshUniversity of Waterloo, Waterloo, Ontario, Canada.
Peter HumphreyYale University, New Haven, Connecticut, USA.
Victor E ReuterMemorial Sloan Kettering, New York, NY, USA.
Lawrence D TrueUniversity of Washington, Seattle, Washington, USA.

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI SELWYN M VICKERS · 1985 to 2026
$347.4M
NCI NIH HHS P30 CA008748
6 · The paper itself

Abstract

Machine learning has been leveraged for image analysis applications throughout a multitude of subspecialties. This position paper provides a perspective on the evolutionary trajectory of practical deep learning tools for genitourinary pathology through evaluating the most recent iterations of such algorithmic devices. Deep learning tools for genitourinary pathology demonstrate potential to enhance prognostic and predictive capacity for tumor assessment including grading, staging, and subtype identification, yet limitations in data availability, regulation, and standardization have stymied their implementation.

Indexed as

Artificial intelligenceComputational pathologyDigital pathologyGenitourinary pathology

Identifiers

PMID36654741
PMCPMC9841212

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.