Evidence map›Paper›PMID 37808782›Full record

ArticlebioRxiv : the preprint server for biology2024

Digital profiling of cancer transcriptomes from histology images with grouped vision attention.

Yuanning Zheng, Marija Pizurica, Francisco Carrillo-Perez, Humaira Noor, Wei Yao, Christian Wohlfart, Kathleen Marchal, Antoaneta Vladimirova, Olivier Gevaert

Open access · greenAbstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed, 7 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors at 4 institutions in 4 countries.

Yuanning ZhengDepartment of Medicine, Stanford Center for Biomedical Informatics Research (BMIR), Stanford University, Stanford, 94305, USA.ORCID 0000-0002-0018-3252
Marija PizuricaDepartment of Medicine, Stanford Center for Biomedical Informatics Research (BMIR), Stanford University, Stanford, 94305, USA.
Francisco Carrillo-PerezDepartment of Medicine, Stanford Center for Biomedical Informatics Research (BMIR), Stanford University, Stanford, 94305, USA.
Humaira NoorDepartment of Medicine, Stanford Center for Biomedical Informatics Research (BMIR), Stanford University, Stanford, 94305, USA.
Wei YaoRoche Information Solutions, Roche Diagnostics Corporation, Santa Clara, California, USA.
Christian WohlfartRoche Diagnostics GmbH, Penzberg, Germany.
Kathleen MarchalInternet technology and Data science Lab (IDLab), Ghent University, Technologiepark-Zwijnaarde 126, Ghent, 9052, Gent, Belgium.
Antoaneta VladimirovaRoche Information Solutions, Roche Diagnostics Corporation, Santa Clara, California, USA.
Olivier GevaertDepartment of Medicine, Stanford Center for Biomedical Informatics Research (BMIR), Stanford University, Stanford, 94305, USA.ORCID 0000-0002-9965-5466
Stanford Medicine · USGhent University · BERoche (Switzerland) · CHRoche Pharma AG (Germany) · DE

Funding

Multi-scale modeling of glioma for the prediction of treatment response, treatment monitoring and treatment allocationR01CA260271 · NCI · STANFORD UNIVERSITY · PI GEVAERT, OLIVIER · 2021 to 2025
$3.1M
NCI NIH HHS R01 CA260271
6 · The paper itself

Abstract

Cancer is a heterogeneous disease that demands precise molecular profiling for better understanding and management. Recently, deep learning has demonstrated potentials for cost-efficient prediction of molecular alterations from histology images. While transformer-based deep learning architectures have enabled significant progress in non-medical domains, their application to histology images remains limited due to small dataset sizes coupled with the explosion of trainable parameters. Here, we develop

Identifiers

PMID37808782
PMCPMC10557714
OpenAlexW4387233997

What Socratic 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 Socratic graph.