Evidence map›Paper›PMID 38534501›Full record

ArticleBioengineering (Basel, Switzerland)2024

Improving Generalizability of PET DL Algorithms: List-Mode Reconstructions Improve DOTATATE PET Hepatic Lesion Detection Performance.

Xinyi Yang, Michael Silosky, Jonathan Wehrend, Daniel V Litwiller, Muthiah Nachiappan, Scott D Metzler, Debashis Ghosh, Fuyong Xing, Bennett B Chin

Open access · goldAbstract 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. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.8field-weighted citation impact, top 29% of its field
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, 2 citations in OpenAlex.

  1. 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

9 authors at 4 institutions in 1 country.

Xinyi YangDepartment of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA.
Michael SiloskyDepartment of Radiology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA.
Jonathan WehrendDepartment of Radiology, Santa Clara Valley Medical Center, San Jose, CA 95128, USA.
Daniel V LitwillerGE HealthCare, Denver, CO 80222, USA.
Muthiah NachiappanDepartment of Radiology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA.
Scott D MetzlerDepartment of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA.
Debashis GhoshDepartment of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA.ORCID 0000-0001-6618-1316
Fuyong XingDepartment of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA.
Bennett B ChinDepartment of Radiology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA.
University of Colorado Anschutz Medical Campus · USUniversity of Colorado Cancer Center · USSanta Clara Valley Medical Center · USUniversity of Pennsylvania · US

Funding

Geles: A Novel Imaging Informatics System for Generalizable Lesion Identification in Neuroendocrine TumorsR21CA274487 · NCI · UNIVERSITY OF COLORADO DENVER · PI CHIN, BENNETT B, XING, FUYONG · 2023 to 2023
$388k
NCI NIH HHS 1 R21 CA274487-01A1NCI NIH HHS R21 CA274487
6 · The paper itself

Abstract

Deep learning (DL) algorithms used for DOTATATE PET lesion detection typically require large, well-annotated training datasets. These are difficult to obtain due to low incidence of gastroenteropancreatic neuroendocrine tumors (GEP-NETs) and the high cost of manual annotation. Furthermore, networks trained and tested with data acquired from site specific PET/CT instrumentation, acquisition and processing protocols have reduced performance when tested with offsite data. This lack of generalizability requires even larger, more diverse training datasets. The objective of this study is to investigate the feasibility of improving DL algorithm performance by better matching the background noise in training datasets to higher noise, out-of-domain testing datasets.

Indexed as

convolutional neural networkdeep learningDOTATATEgastroenteropancreatic neuroendocrine tumorGEP-NETliver tumorPETpositron emission tomography

Identifiers

PMID38534501
PMCPMC10968510
OpenAlexW4392188678

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.