Publication Name	Artificial Intelligence in Medical Imaging
Manuscript ID	59085
Country	Italy

Received	2020-08-23 11:20
Peer-Review Started	2020-08-23 11:21
To Make the First Decision
Return for Revision	2020-09-13 03:02
Revised	2020-09-22 21:09
Second Decision	2020-09-23 03:37
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2020-09-23 05:08
Articles in Press	2020-09-23 05:08
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript	2020-09-27 01:07
Publish the Manuscript Online	2020-09-28 00:58

ISSN	2644-3260 (online)
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/
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Category	Radiology, Nuclear Medicine & Medical Imaging
Manuscript Type	Editorial
Article Title	Current trends of artificial intelligence in cancer imaging
Manuscript Source	Invited Manuscript
All Author List	Francesco Verde, Valeria Romeo, Arnaldo Stanzione and Simone Maurea
Funding Agency and Grant Number
Corresponding Author	Valeria Romeo, MD, PhD, Academic Research, Doctor, Doctor, Research Fellow, Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini 5, Napoli 80131, Italy. valeria.romeo@unina.it
Key Words	Artificial intelligence; Machine learning; Deep learning; Oncology; Medical imaging; Cancer imaging
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.
Publish Date	2020-09-28 00:58
Citation	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
URL	https://www.wjgnet.com/2644-3260/full/v1/i3/87.htm
DOI	https://dx.doi.org/10.35711/aimi.v1.i3.87

Full Article (PDF)	AIMI-1-87.pdf
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Manuscript File	59085_Auto_Edited.docx
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Scientific Misconduct Check	59085-Scientific misconduct check.pdf
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CrossCheck Report	59085-CrossCheck report.pdf