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

Details

Ressource 1Download: s41747-023-00326-z.pdf (2600.53 [Ko])
State: Public
Version: Final published version
License: CC BY 4.0
Serval ID
serval:BIB_EBB87EC8BBD4
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
QuantImage v2: a comprehensive and integrated physician-centered cloud platform for radiomics and machine learning research.
Journal
European radiology experimental
Author(s)
Abler D., Schaer R., Oreiller V., Verma H., Reichenbach J., Aidonopoulos O., Evéquoz F., Jreige M., Prior J.O., Depeursinge A.
ISSN
2509-9280 (Electronic)
ISSN-L
2509-9280
Publication state
Published
Issued date
22/03/2023
Peer-reviewed
Oui
Volume
7
Number
1
Pages
16
Language
english
Notes
Publication types: Journal Article ; Research Support, Non-U.S. Gov't
Publication Status: epublish
Abstract
Radiomics, 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.
We 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.
Support 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.
QI2 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.
Keywords
Radiology/instrumentation, Radiology/methods, Computational Biology, Cloud Computing, Research, Software, Models, Theoretical, Forecasting, Carcinoma/diagnostic imaging, Lung Neoplasms/diagnostic imaging, Humans, Machine Learning, Artificial intelligence, Biomarkers, Cloud computing, Decision support techniques, Radiomics
Pubmed
Web of science
Open Access
Yes
Create date
24/03/2023 9:56
Last modification date
07/09/2023 6:58
Usage data