Cited by 235Radiomics and Machine Learning in Medical ImagingArtificial Intelligence in Healthcare and EducationAI in cancer detection

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Artificial intelligence: Deep learning in oncological radiomics and challenges of interpretability and data harmonization

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Papadimitroulas, Panagiotis, Brocki, Lennart, Christopher Chung, Neo, Marchadour, Wistan, Vermet, Franck, Gaubert, Laurent, Eleftheriadis, Vasilis, Plachouris, Dimitris, Visvikis, Dimitris, Kagadis, George C., Hatt, Mathieu·2021·Physica Medica

DOI 10.1016/j.ejmp.2021.03.009

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Abstract

Over the last decade there has been an extensive evolution in the Artificial Intelligence (AI) field. Modern radiation oncology is based on the exploitation of advanced computational methods aiming to personalization and high diagnostic and therapeutic precision. The quantity of the available imaging data and the increased developments of Machine Learning (ML), particularly Deep Learning (DL), triggered the research on uncovering "hidden" biomarkers and quantitative features from anatomical and functional medical images. Deep Neural Networks (DNN) have achieved outstanding performance and broad implementation in image processing tasks. Lately, DNNs have been considered for radiomics and their potentials for explainable AI (XAI) may help classification and prediction in clinical practice. However, most of them are using limited datasets and lack generalized applicability. In this study we review the basics of radiomics feature extraction, DNNs in image analysis, and major interpretability methods that help enable explainable AI. Furthermore, we discuss the crucial requirement of multicenter recruitment of large datasets, increasing the biomarkers variability, so as to establish the potential clinical value of radiomics and the development of robust explainable AI models.

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Authors: Papadimitroulas, P., Brocki, L., Christopher Chung, N., Marchadour, W., Vermet, F., Gaubert, L., Eleftheriadis, V., Plachouris, D., Visvikis, D., Kagadis, G. C., & Hatt, M.
Affiliations: University of Patras (GR)
University of Warsaw (PL)
Inserm (FR)
Centre National de la Recherche Scientifique (FR)
Corresponding author: Kagadis, George C.
ORCID: Papadimitroulas, Panagiotis
Brocki, Lennart
Marchadour, Wistan
Vermet, Franck
Gaubert, Laurent
Eleftheriadis, Vasilis
Plachouris, Dimitris
Visvikis, Dimitris
Kagadis, George C.
Hatt, Mathieu
Venue: Physica Medica
Publisher: Elsevier BV
Published: 2021-03
Volume: 83
Pages: 108-121
ISSN: 1120-1797 (ISSN-L)
DOI: 10.1016/j.ejmp.2021.03.009
OpenAlex: W3138961261
Cited by: 226
References: 162
Citations by year: 2026: 29 · 2025: 64 · 2024: 42 · 2023: 40 · 2022: 40 · 2021: 11
FWCI: 5.51
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
creativecommons.org/licenses/by/4.0/ (vor, 142 d)
Locations: Physica Medica (publishedVersion, cc-by)
PubMed (publishedVersion)
HAL (submittedVersion)
Funders: European Commission (MIS- 5033021, 872735)
Agence Nationale de la Recherche (ANR-11-LABX-0020)
State Scholarships Foundation (MIS‐5033021, 5033021)
European Social Fund (MIS - 5033021, 5033021)
H2020 Marie Skłodowska-Curie Actions (872735)
CHIST-ERA (CHIST-ERA-19-XAI-007)
Topics: Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
Keywords: radiomics, deep learning, explainable AI, interpretability, data harmonization, radiation oncology, deep neural network, radiomic feature extraction, multi-center dataset, biomarkers, image analysis
MeSH terms: Machine Learning, Deep Learning, Artificial Intelligence, Image Processing, Computer-Assisted, Multicenter Studies as Topic, Neural Networks, Computer

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Artificial intelligence: Deep learning in oncological radiomics and challenges of interpretability and data harmonization

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