Editorial
Copyright ©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.
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
Abstract

In this editorial, we discussed the current research status of artificial intelligence (AI) in Oncology, reviewing the basics of machine learning (ML) and deep learning (DL) techniques and their emerging applications on clinical and imaging cancer workflow. The growing amounts of available “big data” coupled to the increasing computational power have enabled the development of computer-based systems capable to perform advanced tasks in many areas of clinical care, especially in medical imaging. ML is a branch of data science that allows the creation of computer algorithms that can learn and make predictions without prior instructions. DL is a subgroup of artificial neural network algorithms configurated to automatically extract features and perform high-level tasks; convolutional neural networks are the most common DL models used in medical image analysis. AI methods have been proposed in many areas of oncology granting promising results in radiology-based clinical applications. In detail, we explored the emerging applications of AI in oncological risk assessment, lesion detection, characterization, staging, and therapy response. Critical issues such as the lack of reproducibility and generalizability need to be addressed to fully implement AI systems in clinical practice. Nevertheless, AI impact on cancer imaging has been driving the shift of oncology towards a precision diagnostics and personalized cancer treatment.

Keywords: 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.