Evidence map›Paper›PMID 37758869›Full record

ArticleEJNMMI research2023

Sensitivity of an AI method for [

Maria C Ferrández, Sandeep S V Golla, Jakoba J Eertink, Bart M de Vries, Sanne E Wiegers, Gerben J C Zwezerijnen, Simone Pieplenbosch, Louise Schilder, Martijn W Heymans, Josée M Zijlstra and 1 more

Open access · goldAbstract read
In one paragraph

Article in EJNMMI research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.5field-weighted citation impact, top 33% 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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Article
  2. 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 at 4 institutions in 1 country.

Maria C FerrándezCancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands. m.c.ferrandezferrandez@amsterdamumc.nl.
Sandeep S V GollaCancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
Jakoba J EertinkCancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands.
Bart M de VriesCancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
Sanne E WiegersCancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
Gerben J C ZwezerijnenCancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
Simone PieplenboschCancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands.
Louise SchilderDepartment of Internal Medicine, Amstelland Hospital, Amstelveen, The Netherlands.
Martijn W HeymansDepartment of Epidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
Josée M ZijlstraCancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands.
Ronald BoellaardCancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
Dutch Cancer Society · NLVrije Universiteit Amsterdam · NLCancer Center AmsterdamZiekenhuis Amstelland · NL

Funding

KWF Kankerbestrijding VU-2018-11648
6 · The paper itself

Abstract

backgroundConvolutional neural networks (CNNs), applied to baseline [

resultsCNN probabilities were found to be sensitive to different reconstruction protocols (EARL2 ΔP: median = 0.09, interquartile range (IQR) = [0.06, 0.10] and HR ΔP: median = 0.1, IQR = [0.08, 0.16]). Moreover, higher resolution images (EARL2 and HR) led to higher probability values. After image-based and ComBat transformation, an improved agreement of CNN probabilities among reconstructions was found for all patients. This agreement was slightly better after image-based transformation (transformed EARL2 ΔP: median = 0.022, IQR = [0.01, 0.02] and transformed HR ΔP: median = 0.029, IQR = [0.01, 0.03]).

conclusionOur CNN-based outcome predictions are affected by the applied reconstruction protocols, yet in a predictable manner. Image-based harmonization is a suitable approach to harmonize CNN predictions across image reconstruction protocols.

Indexed as

Convolutional neural networksDiffuse large B-cell lymphomaPETReconstruction

Identifiers

PMID37758869
PMCPMC10533444
OpenAlexW4387114800

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.