Evidence map›Paper›PMID 36947346›Full record

ArticleEuropean radiology experimental2023

QuantImage v2: a comprehensive and integrated physician-centered cloud platform for radiomics and machine learning research.

Daniel Abler, Roger Schaer, Valentin Oreiller, Himanshu Verma, Julien Reichenbach, Orfeas Aidonopoulos, Florian Evéquoz, Mario Jreige, John O Prior, Adrien Depeursinge

Full text read
In one paragraph

Article in European radiology experimental, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Function ofEJNMMI reports · 2025
    Article
  5. MRI and CT radiomics for the diagnosis of acute pancreatitis.European journal of radiology open · 2025
    Article
  6. Article
  7. 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

10 authors.

Daniel Abler *Institute of Informatics, School of Management, HES-SO Valais-Wallis, Sierre, Switzerland.ORCID 0000-0003-1776-5985
Roger Schaer *Institute of Informatics, School of Management, HES-SO Valais-Wallis, Sierre, Switzerland.ORCID 0000-0001-7984-2099
Valentin OreillerInstitute of Informatics, School of Management, HES-SO Valais-Wallis, Sierre, Switzerland.ORCID 0000-0002-7794-6916
Himanshu VermaKnowledge and Intelligence Design Group, Delft University of Technology, Delft, The Netherlands.ORCID 0000-0002-2494-1556
Julien ReichenbachInstitute of Informatics, School of Management, HES-SO Valais-Wallis, Sierre, Switzerland.ORCID 0000-0003-0978-7017
Orfeas AidonopoulosInstitute of Informatics, School of Management, HES-SO Valais-Wallis, Sierre, Switzerland.
Florian EvéquozInstitute of Informatics, School of Management, HES-SO Valais-Wallis, Sierre, Switzerland.ORCID 0000-0002-6052-2252
Mario JreigeDepartment of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.ORCID 0000-0003-3918-150X
John O PriorDepartment of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.ORCID 0000-0003-1429-1374
Adrien DepeursingeInstitute of Informatics, School of Management, HES-SO Valais-Wallis, Sierre, Switzerland. adrien.depeursinge@hevs.ch.ORCID 0000-0002-2362-0304

Funding

Hasler Stiftung EPICSHasler Stiftung MSXplainSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung 205320/179069Swiss Personalized Health Network (SPHN) IMAGINESwiss Personalized Health Network (SPHN) QA4IQI
6 · The paper itself

Abstract

backgroundRadiomics, the field of image-based computational medical biomarker research, has experienced rapid growth over the past decade due to its potential to revolutionize the development of personalized decision support models. However, despite its research momentum and important advances toward methodological standardization, the translation of radiomics prediction models into clinical practice only progresses slowly. The lack of physicians leading the development of radiomics models and insufficient integration of radiomics tools in the clinical workflow contributes to this slow uptake.

methodsWe propose a physician-centered vision of radiomics research and derive minimal functional requirements for radiomics research software to support this vision. Free-to-access radiomics tools and frameworks were reviewed to identify best practices and reveal the shortcomings of existing software solutions to optimally support physician-driven radiomics research in a clinical environment.

resultsSupport for user-friendly development and evaluation of radiomics prediction models via machine learning was found to be missing in most tools. QuantImage v2 (QI2) was designed and implemented to address these shortcomings. QI2 relies on well-established existing tools and open-source libraries to realize and concretely demonstrate the potential of a one-stop tool for physician-driven radiomics research. It provides web-based access to cohort management, feature extraction, and visualization and supports "no-code" development and evaluation of machine learning models against patient-specific outcome data.

conclusionsQI2 fills a gap in the radiomics software landscape by enabling "no-code" radiomics research, including model validation, in a clinical environment. Further information about QI2, a public instance of the system, and its source code is available at https://medgift.github.io/quantimage-v2-info/ . Key points As domain experts, physicians play a key role in the development of radiomics models. Existing software solutions do not support physician-driven research optimally. QuantImage v2 implements a physician-centered vision for radiomics research. QuantImage v2 is a web-based, "no-code" radiomics research platform.

Indexed as

Cloud ComputingComputational BiologyRadiologyCarcinomaForecastingHumansLung NeoplasmsMachine LearningModels, TheoreticalResearchSoftwareArtificial intelligenceBiomarkersCloud computingDecision support techniquesRadiomics

Identifiers

PMID36947346
PMCPMC10033788

What OpenQuestion holds

Textfull text, public
LicenceCC BY
measurements read10
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.