Evidence map›Paper›PMID 34503100›Full record

ArticleCancers2021

PTEN and DNA Ploidy Status by Machine Learning in Prostate Cancer.

Karolina Cyll, Andreas Kleppe, Joakim Kalsnes, Ljiljana Vlatkovic, Manohar Pradhan, Wanja Kildal, Kari Anne R Tobin, Trine M Reine, Håkon Wæhre, Bjørn Brennhovd and 4 more

Abstract read
In one paragraph

Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Artificial Intelligence-Based PTEN Loss Assessment as an Early Predictor of Prostate Cancer Metastasis After Surgery: A Multicenter Retrospective Study.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2023
    Article
  8. Article
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

14 authors.

Karolina CyllInstitute for Cancer Genetics and Informatics, Oslo University Hospital, NO-0424 Oslo, Norway.
Andreas KleppeInstitute for Cancer Genetics and Informatics, Oslo University Hospital, NO-0424 Oslo, Norway.ORCID 0000-0002-8370-5289
Joakim KalsnesInstitute for Cancer Genetics and Informatics, Oslo University Hospital, NO-0424 Oslo, Norway.
Ljiljana VlatkovicInstitute for Cancer Genetics and Informatics, Oslo University Hospital, NO-0424 Oslo, Norway.
Manohar PradhanInstitute for Cancer Genetics and Informatics, Oslo University Hospital, NO-0424 Oslo, Norway.
Wanja KildalInstitute for Cancer Genetics and Informatics, Oslo University Hospital, NO-0424 Oslo, Norway.ORCID 0000-0002-9639-6868
Kari Anne R TobinInstitute for Cancer Genetics and Informatics, Oslo University Hospital, NO-0424 Oslo, Norway.
Trine M ReineInstitute for Cancer Genetics and Informatics, Oslo University Hospital, NO-0424 Oslo, Norway.
Håkon WæhreInstitute for Cancer Genetics and Informatics, Oslo University Hospital, NO-0424 Oslo, Norway.
Bjørn BrennhovdDepartment of Urology, Oslo University Hospital, NO-0424 Oslo, Norway.
Hanne A AskautrudInstitute for Cancer Genetics and Informatics, Oslo University Hospital, NO-0424 Oslo, Norway.
Erik Skaaheim HaugInstitute for Cancer Genetics and Informatics, Oslo University Hospital, NO-0424 Oslo, Norway.
Tarjei S HveemInstitute for Cancer Genetics and Informatics, Oslo University Hospital, NO-0424 Oslo, Norway.
Håvard E DanielsenInstitute for Cancer Genetics and Informatics, Oslo University Hospital, NO-0424 Oslo, Norway.

Funding

Helse Sør-Øst RHF 2013133Norges Forskningsråd 259204
6 · The paper itself

Abstract

Machine learning (ML) is expected to improve biomarker assessment. Using convolution neural networks, we developed a fully-automated method for assessing PTEN protein status in immunohistochemically-stained slides using a radical prostatectomy (RP) cohort (

Indexed as

DNA ploidymachine learningprostate cancerPTENtumor heterogeneity

Identifiers

PMID34503100
PMCPMC8428363

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.