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Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential

The high-throughput extraction of quantitative imaging features from medical images for the purpose of radiomic analysis, i.e., radiomics in a broad sense, is a rapidly developing and emerging research field that has been attracting increasing interest, particularly in multimodality and multi-omics...

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Autores principales: Zhang, Xingping, Zhang, Yanchun, Zhang, Guijuan, Qiu, Xingting, Tan, Wenjun, Yin, Xiaoxia, Liao, Liefa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8891653/
https://www.ncbi.nlm.nih.gov/pubmed/35251962
http://dx.doi.org/10.3389/fonc.2022.773840
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author Zhang, Xingping
Zhang, Yanchun
Zhang, Guijuan
Qiu, Xingting
Tan, Wenjun
Yin, Xiaoxia
Liao, Liefa
author_facet Zhang, Xingping
Zhang, Yanchun
Zhang, Guijuan
Qiu, Xingting
Tan, Wenjun
Yin, Xiaoxia
Liao, Liefa
author_sort Zhang, Xingping
collection PubMed
description The high-throughput extraction of quantitative imaging features from medical images for the purpose of radiomic analysis, i.e., radiomics in a broad sense, is a rapidly developing and emerging research field that has been attracting increasing interest, particularly in multimodality and multi-omics studies. In this context, the quantitative analysis of multidimensional data plays an essential role in assessing the spatio-temporal characteristics of different tissues and organs and their microenvironment. Herein, recent developments in this method, including manually defined features, data acquisition and preprocessing, lesion segmentation, feature extraction, feature selection and dimension reduction, statistical analysis, and model construction, are reviewed. In addition, deep learning-based techniques for automatic segmentation and radiomic analysis are being analyzed to address limitations such as rigorous workflow, manual/semi-automatic lesion annotation, and inadequate feature criteria, and multicenter validation. Furthermore, a summary of the current state-of-the-art applications of this technology in disease diagnosis, treatment response, and prognosis prediction from the perspective of radiology images, multimodality images, histopathology images, and three-dimensional dose distribution data, particularly in oncology, is presented. The potential and value of radiomics in diagnostic and therapeutic strategies are also further analyzed, and for the first time, the advances and challenges associated with dosiomics in radiotherapy are summarized, highlighting the latest progress in radiomics. Finally, a robust framework for radiomic analysis is presented and challenges and recommendations for future development are discussed, including but not limited to the factors that affect model stability (medical big data and multitype data and expert knowledge in medical), limitations of data-driven processes (reproducibility and interpretability of studies, different treatment alternatives for various institutions, and prospective researches and clinical trials), and thoughts on future directions (the capability to achieve clinical applications and open platform for radiomics analysis).
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spelling pubmed-88916532022-03-04 Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential Zhang, Xingping Zhang, Yanchun Zhang, Guijuan Qiu, Xingting Tan, Wenjun Yin, Xiaoxia Liao, Liefa Front Oncol Oncology The high-throughput extraction of quantitative imaging features from medical images for the purpose of radiomic analysis, i.e., radiomics in a broad sense, is a rapidly developing and emerging research field that has been attracting increasing interest, particularly in multimodality and multi-omics studies. In this context, the quantitative analysis of multidimensional data plays an essential role in assessing the spatio-temporal characteristics of different tissues and organs and their microenvironment. Herein, recent developments in this method, including manually defined features, data acquisition and preprocessing, lesion segmentation, feature extraction, feature selection and dimension reduction, statistical analysis, and model construction, are reviewed. In addition, deep learning-based techniques for automatic segmentation and radiomic analysis are being analyzed to address limitations such as rigorous workflow, manual/semi-automatic lesion annotation, and inadequate feature criteria, and multicenter validation. Furthermore, a summary of the current state-of-the-art applications of this technology in disease diagnosis, treatment response, and prognosis prediction from the perspective of radiology images, multimodality images, histopathology images, and three-dimensional dose distribution data, particularly in oncology, is presented. The potential and value of radiomics in diagnostic and therapeutic strategies are also further analyzed, and for the first time, the advances and challenges associated with dosiomics in radiotherapy are summarized, highlighting the latest progress in radiomics. Finally, a robust framework for radiomic analysis is presented and challenges and recommendations for future development are discussed, including but not limited to the factors that affect model stability (medical big data and multitype data and expert knowledge in medical), limitations of data-driven processes (reproducibility and interpretability of studies, different treatment alternatives for various institutions, and prospective researches and clinical trials), and thoughts on future directions (the capability to achieve clinical applications and open platform for radiomics analysis). Frontiers Media S.A. 2022-02-17 /pmc/articles/PMC8891653/ /pubmed/35251962 http://dx.doi.org/10.3389/fonc.2022.773840 Text en Copyright © 2022 Zhang, Zhang, Zhang, Qiu, Tan, Yin and Liao https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Zhang, Xingping
Zhang, Yanchun
Zhang, Guijuan
Qiu, Xingting
Tan, Wenjun
Yin, Xiaoxia
Liao, Liefa
Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential
title Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential
title_full Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential
title_fullStr Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential
title_full_unstemmed Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential
title_short Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential
title_sort deep learning with radiomics for disease diagnosis and treatment: challenges and potential
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8891653/
https://www.ncbi.nlm.nih.gov/pubmed/35251962
http://dx.doi.org/10.3389/fonc.2022.773840
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