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Radiomics, a Promising New Discipline: Example of Hepatocellular Carcinoma
Radiomics is a discipline that involves studying medical images through their digital data. Using “artificial intelligence” algorithms, radiomics utilizes quantitative and high-throughput analysis of an image’s textural richness to obtain relevant information for clinicians, from diagnosis assistanc...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10093101/ https://www.ncbi.nlm.nih.gov/pubmed/37046521 http://dx.doi.org/10.3390/diagnostics13071303 |
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author | Lévi-Strauss, Thomas Tortorici, Bettina Lopez, Olivier Viau, Philippe Ouizeman, Dann J. Schall, Baptiste Adhoute, Xavier Humbert, Olivier Chevallier, Patrick Gual, Philippe Fillatre, Lionel Anty, Rodolphe |
author_facet | Lévi-Strauss, Thomas Tortorici, Bettina Lopez, Olivier Viau, Philippe Ouizeman, Dann J. Schall, Baptiste Adhoute, Xavier Humbert, Olivier Chevallier, Patrick Gual, Philippe Fillatre, Lionel Anty, Rodolphe |
author_sort | Lévi-Strauss, Thomas |
collection | PubMed |
description | Radiomics is a discipline that involves studying medical images through their digital data. Using “artificial intelligence” algorithms, radiomics utilizes quantitative and high-throughput analysis of an image’s textural richness to obtain relevant information for clinicians, from diagnosis assistance to therapeutic guidance. Exploitation of these data could allow for a more detailed characterization of each phenotype, for each patient, making radiomics a new biomarker of interest, highly promising in the era of precision medicine. Moreover, radiomics is non-invasive, cost-effective, and easily reproducible in time. In the field of oncology, it performs an analysis of the entire tumor, which is impossible with a single biopsy but is essential for understanding the tumor’s heterogeneity and is known to be closely related to prognosis. However, current results are sometimes less accurate than expected and often require the addition of non-radiomics data to create a performing model. To highlight the strengths and weaknesses of this new technology, we take the example of hepatocellular carcinoma and show how radiomics could facilitate its diagnosis in difficult cases, predict certain histological features, and estimate treatment response, whether medical or surgical. |
format | Online Article Text |
id | pubmed-10093101 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-100931012023-04-13 Radiomics, a Promising New Discipline: Example of Hepatocellular Carcinoma Lévi-Strauss, Thomas Tortorici, Bettina Lopez, Olivier Viau, Philippe Ouizeman, Dann J. Schall, Baptiste Adhoute, Xavier Humbert, Olivier Chevallier, Patrick Gual, Philippe Fillatre, Lionel Anty, Rodolphe Diagnostics (Basel) Review Radiomics is a discipline that involves studying medical images through their digital data. Using “artificial intelligence” algorithms, radiomics utilizes quantitative and high-throughput analysis of an image’s textural richness to obtain relevant information for clinicians, from diagnosis assistance to therapeutic guidance. Exploitation of these data could allow for a more detailed characterization of each phenotype, for each patient, making radiomics a new biomarker of interest, highly promising in the era of precision medicine. Moreover, radiomics is non-invasive, cost-effective, and easily reproducible in time. In the field of oncology, it performs an analysis of the entire tumor, which is impossible with a single biopsy but is essential for understanding the tumor’s heterogeneity and is known to be closely related to prognosis. However, current results are sometimes less accurate than expected and often require the addition of non-radiomics data to create a performing model. To highlight the strengths and weaknesses of this new technology, we take the example of hepatocellular carcinoma and show how radiomics could facilitate its diagnosis in difficult cases, predict certain histological features, and estimate treatment response, whether medical or surgical. MDPI 2023-03-30 /pmc/articles/PMC10093101/ /pubmed/37046521 http://dx.doi.org/10.3390/diagnostics13071303 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Lévi-Strauss, Thomas Tortorici, Bettina Lopez, Olivier Viau, Philippe Ouizeman, Dann J. Schall, Baptiste Adhoute, Xavier Humbert, Olivier Chevallier, Patrick Gual, Philippe Fillatre, Lionel Anty, Rodolphe Radiomics, a Promising New Discipline: Example of Hepatocellular Carcinoma |
title | Radiomics, a Promising New Discipline: Example of Hepatocellular Carcinoma |
title_full | Radiomics, a Promising New Discipline: Example of Hepatocellular Carcinoma |
title_fullStr | Radiomics, a Promising New Discipline: Example of Hepatocellular Carcinoma |
title_full_unstemmed | Radiomics, a Promising New Discipline: Example of Hepatocellular Carcinoma |
title_short | Radiomics, a Promising New Discipline: Example of Hepatocellular Carcinoma |
title_sort | radiomics, a promising new discipline: example of hepatocellular carcinoma |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10093101/ https://www.ncbi.nlm.nih.gov/pubmed/37046521 http://dx.doi.org/10.3390/diagnostics13071303 |
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