Evidence map›Paper›PMID 38284821›Full record

ArticleMolecular cancer research : MCR2024

Semi-Supervised, Attention-Based Deep Learning for Predicting TMPRSS2:ERG Fusion Status in Prostate Cancer Using Whole Slide Images.

Mohamed Omar, Zhuoran Xu, Sophie B Rand, Mohammad K Alexanderani, Daniela C Salles, Itzel Valencia, Edward M Schaeffer, Brian D Robinson, Tamara L Lotan, Massimo Loda and 1 more

Abstract read
In one paragraph

Article in Molecular cancer research : MCR, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–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 synthesis or guideline pooled it.

  1. Pooled it
  2. Immune Spatial Organization Predicts Distant Metastasis Risk in Aggressive Localized Prostate Cancer.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026
    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

11 authors.

Mohamed OmarDepartment of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, New York.ORCID 0000-0002-8068-1920
Zhuoran XuDepartment of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, New York.ORCID 0000-0001-6733-541X
Sophie B RandDepartment of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, New York.ORCID 0000-0003-0941-208X
Mohammad K AlexanderaniDepartment of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, New York.ORCID 0009-0006-1018-3033
Daniela C SallesDepartment of Pathology, Johns Hopkins University, Baltimore, Maryland.ORCID 0000-0003-1904-185X
Itzel ValenciaDepartment of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, New York.ORCID 0000-0002-7450-9307
Edward M SchaefferFeinberg School of Medicine, Northwestern University, Chicago, Illinois.ORCID 0000-0003-0699-1899
Brian D RobinsonDepartment of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, New York.ORCID 0000-0001-5374-890X
Tamara L LotanDepartment of Pathology, Johns Hopkins University, Baltimore, Maryland.ORCID 0000-0002-0494-9067
Massimo LodaDepartment of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, New York.ORCID 0000-0001-9674-8379
Luigi MarchionniDepartment of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, New York.ORCID 0000-0002-7336-8071

Funding

Resource Sharing CoreU54CA273956 · NCI · UNIVERSITY OF MARYLAND BALTIMORE · PI Phuoc T. Tran · 2022 to 2026
$8.9M
IMMUNOLOGY RESEARCH TRAINING PROGRAMT32AI007621 · NIAID · WEILL MEDICAL COLL OF CORNELL UNIV · PI CESARMAN, ETHEL · 1999 to 2014
$3.4M
Hardwiring Mechanism into Predicting Cancer Phenotypes by Computational LearningR01CA200859 · NCI · WEILL MEDICAL COLL OF CORNELL UNIV · PI MARCHIONNI, LUIGI · 2016 to 2020
$1.9M
Next-Gen Oncopathology ProgramT32CA260293 · NCI · WEILL MEDICAL COLL OF CORNELL UNIV · PI Ethel Cesarman, BENJAMIN MONTEVERDE KLEAVELAND · 2022 to 2026
$1.4M
NCI NIH HHS R01 CA200859NCI NIH HHS T32 CA260293NCI NIH HHS U54 CA273956NIAID NIH HHS T32 AI007621
6 · The paper itself

Abstract

implicationsOur study illuminates the potential of deep learning in effectively inferring key prostate cancer genetic alterations from the tissue morphology depicted in routinely available histology slides, offering a cost-effective method that could revolutionize diagnostic strategies in oncology.

Indexed as

Deep LearningProstatic NeoplasmsHumansMaleOncogene Proteins, FusionProstatectomySerine EndopeptidasesTranscriptional Regulator ERGERG protein, humanOncogene Proteins, FusionSerine EndopeptidasesTMPRSS2 protein, humanTranscriptional Regulator ERG

Identifiers

PMID38284821
PMCPMC10985477

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.