Verde F, Romeo V, Stanzione A, Maurea S. Current trends of artificial intelligence in cancer imaging. Artif Intell Med Imaging 2020; 1(3): 87-93 [DOI: 10.35711/aimi.v1.i3.87]
Corresponding Author of This Article
Valeria Romeo, MD, PhD, Academic Research, Doctor, Research Fellow, Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini 5, Napoli 80131, Italy. valeria.romeo@unina.it
Research Domain of This Article
Radiology, Nuclear Medicine & Medical Imaging
Article-Type of This Article
Editorial
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Artif Intell Med Imaging. Sep 28, 2020; 1(3): 87-93 Published online Sep 28, 2020. doi: 10.35711/aimi.v1.i3.87
Current trends of artificial intelligence in cancer imaging
Francesco Verde, Valeria Romeo, Arnaldo Stanzione, Simone Maurea
Francesco Verde, Valeria Romeo, Arnaldo Stanzione, Simone Maurea, Department of Advanced Biomedical Sciences, University of Naples "Federico II", Napoli 80131, Italy
Author contributions: Verde F drafted the manuscript; Romeo V conceptualized and drafted the manuscript; Stanzione A and Maurea S performed critical revision and approved the final manuscript.
Conflict-of-interest statement: The authors declare no conflicts of interest.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Valeria Romeo, MD, PhD, Academic Research, Doctor, Research Fellow, Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini 5, Napoli 80131, Italy. valeria.romeo@unina.it
Received: August 23, 2020 Peer-review started: August 23, 2020 First decision: September 13, 2020 Revised: September 22, 2020 Accepted: September 23, 2020 Article in press: September 23, 2020 Published online: September 28, 2020 Processing time: 35 Days and 13.6 Hours
Core Tip
Core Tip: Advanced computational systems and availability of multi-dimensional data have led the possibility of artificial intelligence (AI) consisting of machine learning (ML) and deep learning (DL) algorithms to be implemented in healthcare data analysis, with reliable results in the oncology field and particularly in diagnostic imaging tasks. Supervised algorithms are the most common ML models used in medical image analysis, while convolutional neural networks are the main DL approach. AI-based models have demonstrated outperforming results in oncological risk assessment, lesion detection, segmentation, characterization, staging, and therapy response. Growing emerging evidence supports the leading role of AI in all cancer imaging pathways from screening programs to diagnostic and prognostic tasks, boosting the paradigm of precision medicine.
