Evidence map›Paper›PMID 40342794›Full record

ArticleApplications of Medical Artificial Intelligence : Second International Workshop, AMAI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings2024

Anatomical Location-Guided Deep Learning-Based Genetic Cluster Identification of Pheochromocytomas and Paragangliomas From CT Images.

Bikash Santra, Abhishek Jha, Pritam Mukherjee, Mayank Patel, Karel Pacak, Ronald M Summers

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Applications of Medical Artificial Intelligence : Second International Workshop, AMAI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Weakly supervised detection of pheochromocytomas and paragangliomas in CT using noisy data.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2024
    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

6 authors.

Bikash SantraImaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA.
Abhishek JhaNational Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA.
Pritam MukherjeeImaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA.
Mayank PatelNational Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA.
Karel PacakNational Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA.
Ronald M SummersImaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA.

Funding

Computer Aided Detection for Radiologic ImagesZ01CL040004 · CLC · CLINICAL CENTER · PI SUMMERS, RONALD M. · 2003 to 2008
$41k
Intramural NIH HHS Z01 CL040004
6 · The paper itself

Abstract

Pheochromocytomas and paragangliomas (PPGLs) are respectively intra-adrenal and extra-adrenal neuroendocrine tumors whose pathogenesis and progression are greatly regulated by genetics. Identifying PPGL's genetic clusters (

Indexed as

CT imagesDeep learningGenetic mutationsParagangliomaPheochromocytomaPPGLRadiogenomicsTransformer

Identifiers

PMID40342794
PMCPMC12060173

What OpenQuestion holds

Textmetadata
LicenceTDM
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.