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Radiomics at a Glance: A Few Lessons Learned from Learning Approaches
SIMPLE SUMMARY: Radiomics has become a prominent component of medical imaging research and many studies show its specific value as a support tool for clinical decision-making processes. Radiomic data are typically analyzed with statistical and machine learning methods, which change depending on the...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7563283/ https://www.ncbi.nlm.nih.gov/pubmed/32872466 http://dx.doi.org/10.3390/cancers12092453 |
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author | Capobianco, Enrico Deng, Jun |
author_facet | Capobianco, Enrico Deng, Jun |
author_sort | Capobianco, Enrico |
collection | PubMed |
description | SIMPLE SUMMARY: Radiomics has become a prominent component of medical imaging research and many studies show its specific value as a support tool for clinical decision-making processes. Radiomic data are typically analyzed with statistical and machine learning methods, which change depending on the disease context and the imaging modality. We found a certain bias in the literature towards the use of such methods and believe that this limitation may influence the capacity of producing accurate and reliable decisions. Therefore, in view of the relevance of various types of learning methods, we report their significance and discuss their unrevealed potential. ABSTRACT: Processing and modeling medical images have traditionally represented complex tasks requiring multidisciplinary collaboration. The advent of radiomics has assigned a central role to quantitative data analytics targeting medical image features algorithmically extracted from large volumes of images. Apart from the ultimate goal of supporting diagnostic, prognostic, and therapeutic decisions, radiomics is computationally attractive due to specific strengths: scalability, efficiency, and precision. Optimization is achieved by highly sophisticated statistical and machine learning algorithms, but it is especially deep learning that stands out as the leading inference approach. Various types of hybrid learning can be considered when building complex integrative approaches aimed to deliver gains in accuracy for both classification and prediction tasks. This perspective reviews some selected learning methods by focusing on both their significance for radiomics and their unveiled potential. |
format | Online Article Text |
id | pubmed-7563283 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75632832020-10-27 Radiomics at a Glance: A Few Lessons Learned from Learning Approaches Capobianco, Enrico Deng, Jun Cancers (Basel) Perspective SIMPLE SUMMARY: Radiomics has become a prominent component of medical imaging research and many studies show its specific value as a support tool for clinical decision-making processes. Radiomic data are typically analyzed with statistical and machine learning methods, which change depending on the disease context and the imaging modality. We found a certain bias in the literature towards the use of such methods and believe that this limitation may influence the capacity of producing accurate and reliable decisions. Therefore, in view of the relevance of various types of learning methods, we report their significance and discuss their unrevealed potential. ABSTRACT: Processing and modeling medical images have traditionally represented complex tasks requiring multidisciplinary collaboration. The advent of radiomics has assigned a central role to quantitative data analytics targeting medical image features algorithmically extracted from large volumes of images. Apart from the ultimate goal of supporting diagnostic, prognostic, and therapeutic decisions, radiomics is computationally attractive due to specific strengths: scalability, efficiency, and precision. Optimization is achieved by highly sophisticated statistical and machine learning algorithms, but it is especially deep learning that stands out as the leading inference approach. Various types of hybrid learning can be considered when building complex integrative approaches aimed to deliver gains in accuracy for both classification and prediction tasks. This perspective reviews some selected learning methods by focusing on both their significance for radiomics and their unveiled potential. MDPI 2020-08-29 /pmc/articles/PMC7563283/ /pubmed/32872466 http://dx.doi.org/10.3390/cancers12092453 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Perspective Capobianco, Enrico Deng, Jun Radiomics at a Glance: A Few Lessons Learned from Learning Approaches |
title | Radiomics at a Glance: A Few Lessons Learned from Learning Approaches |
title_full | Radiomics at a Glance: A Few Lessons Learned from Learning Approaches |
title_fullStr | Radiomics at a Glance: A Few Lessons Learned from Learning Approaches |
title_full_unstemmed | Radiomics at a Glance: A Few Lessons Learned from Learning Approaches |
title_short | Radiomics at a Glance: A Few Lessons Learned from Learning Approaches |
title_sort | radiomics at a glance: a few lessons learned from learning approaches |
topic | Perspective |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7563283/ https://www.ncbi.nlm.nih.gov/pubmed/32872466 http://dx.doi.org/10.3390/cancers12092453 |
work_keys_str_mv | AT capobiancoenrico radiomicsataglanceafewlessonslearnedfromlearningapproaches AT dengjun radiomicsataglanceafewlessonslearnedfromlearningapproaches |