Evidence map›Paper›PMID 38790363›Full record

ArticleBioengineering (Basel, Switzerland)2024

Improving the Generalizability of Deep Learning for T2-Lesion Segmentation of Gliomas in the Post-Treatment Setting.

Jacob Ellison, Francesco Caliva, Pablo Damasceno, Tracy L Luks, Marisa LaFontaine, Julia Cluceru, Anil Kemisetti, Yan Li, Annette M Molinaro, Valentina Pedoia and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Bioengineering (Basel, Switzerland), 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.

No citing paper in PubMed yet.

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

12 authors.

Jacob EllisonDepartment of Radiology and Biomedical Imaging, UCSF, San Francisco, CA 94143, USA.ORCID 0000-0003-1164-332X
Francesco CalivaDepartment of Radiology and Biomedical Imaging, UCSF, San Francisco, CA 94143, USA.ORCID 0000-0002-0425-7511
Pablo DamascenoDepartment of Radiology and Biomedical Imaging, UCSF, San Francisco, CA 94143, USA.ORCID 0000-0001-5615-121X
Tracy L LuksDepartment of Radiology and Biomedical Imaging, UCSF, San Francisco, CA 94143, USA.
Marisa LaFontaineDepartment of Radiology and Biomedical Imaging, UCSF, San Francisco, CA 94143, USA.ORCID 0000-0002-2352-3299
Julia CluceruDepartment of Radiology and Biomedical Imaging, UCSF, San Francisco, CA 94143, USA.
Anil KemisettiDepartment of Radiology and Biomedical Imaging, UCSF, San Francisco, CA 94143, USA.
Yan LiDepartment of Radiology and Biomedical Imaging, UCSF, San Francisco, CA 94143, USA.ORCID 0000-0003-2145-2869
Annette M MolinaroDepartment of Neurological Surgery, UCSF, San Francisco, CA 94143, USA.
Valentina PedoiaDepartment of Radiology and Biomedical Imaging, UCSF, San Francisco, CA 94143, USA.
Javier E Villanueva-MeyerDepartment of Radiology and Biomedical Imaging, UCSF, San Francisco, CA 94143, USA.
Janine M LupoDepartment of Radiology and Biomedical Imaging, UCSF, San Francisco, CA 94143, USA.ORCID 0000-0002-0051-6387

Funding

Project 4: Multi-modality Metabolic Imaging for Monitoring Molecular Sub-types of GliomaP01CA118816 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Susan M Chang, Janine Marie Lupo · 2007 to 2026
$44.7M
Training Program in Translational Brain Tumor ResearchT32CA151022 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Joseph F Costello · 2010 to 2026
$6.6M
Department of Defense HT9425-23-1-0510, HT9425-23-1-0511, HT9425-23-1-0512NCI NIH HHS P01 CA118816NCI NIH HHS T32 CA151022NIH HHS P01CA118816UCSF Helen Diller Family Cancer Center Cancer Imaging Resources Pilot Grant
6 · The paper itself

Abstract

Although fully automated volumetric approaches for monitoring brain tumor response have many advantages, most available deep learning models are optimized for highly curated, multi-contrast MRI from newly diagnosed gliomas, which are not representative of post-treatment cases in the clinic. Improving segmentation for treated patients is critical to accurately tracking changes in response to therapy. We investigated mixing data from newly diagnosed (

Indexed as

deep learninggliomapost-treatmentsegmentation

Identifiers

PMID38790363
PMCPMC11117752

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.