• Panagiotis Papadimitroulas, Lennart Brocki, Christopher ChungNeoNUniversity of Warsaw - Institute of Informatics, Warsaw, Poland; University of California Los Angeles (UCLA) School of Medicine - Departments of Physiology and Medicine (Cardiology), USA., Wistan Marchadour, Franck Vermet, Laurent Gaubert, Vasilis Eleftheriadis, Dimitris Plachouris, Dimitris Visvikis, George C Kagadis, and Mathieu Hatt.
• Bioemission Technology Solutions - BIOEMTECH, Athens, Greece; 3DMI Research Group, Department of Medical Physics, University of Patras, Rion GR 265 04, Greece.
• Phys Med. 2021 Mar 1; 83: 108-121.

AbstractOver 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.Copyright © 2021 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

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