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Radiomics in Head and Neck Cancer Outcome Predictions
Head and neck cancer has great regional anatomical complexity, as it can develop in different structures, exhibiting diverse tumour manifestations and high intratumoural heterogeneity, which is highly related to resistance to treatment, progression, the appearance of metastases, and tumour recurrenc...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9689406/ https://www.ncbi.nlm.nih.gov/pubmed/36359576 http://dx.doi.org/10.3390/diagnostics12112733 |
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author | Gonçalves, Maria Gsaxner, Christina Ferreira, André Li, Jianning Puladi, Behrus Kleesiek, Jens Egger, Jan Alves, Victor |
author_facet | Gonçalves, Maria Gsaxner, Christina Ferreira, André Li, Jianning Puladi, Behrus Kleesiek, Jens Egger, Jan Alves, Victor |
author_sort | Gonçalves, Maria |
collection | PubMed |
description | Head and neck cancer has great regional anatomical complexity, as it can develop in different structures, exhibiting diverse tumour manifestations and high intratumoural heterogeneity, which is highly related to resistance to treatment, progression, the appearance of metastases, and tumour recurrences. Radiomics has the potential to address these obstacles by extracting quantitative, measurable, and extractable features from the region of interest in medical images. Medical imaging is a common source of information in clinical practice, presenting a potential alternative to biopsy, as it allows the extraction of a large number of features that, although not visible to the naked eye, may be relevant for tumour characterisation. Taking advantage of machine learning techniques, the set of features extracted when associated with biological parameters can be used for diagnosis, prognosis, and predictive accuracy valuable for clinical decision-making. Therefore, the main goal of this contribution was to determine to what extent the features extracted from Computed Tomography (CT) are related to cancer prognosis, namely Locoregional Recurrences (LRs), the development of Distant Metastases (DMs), and Overall Survival (OS). Through the set of tumour characteristics, predictive models were developed using machine learning techniques. The tumour was described by radiomic features, extracted from images, and by the clinical data of the patient. The performance of the models demonstrated that the most successful algorithm was XGBoost, and the inclusion of the patients’ clinical data was an asset for cancer prognosis. Under these conditions, models were created that can reliably predict the LR, DM, and OS status, with the area under the ROC curve (AUC) values equal to 0.74, 0.84, and 0.91, respectively. In summary, the promising results obtained show the potential of radiomics, once the considered cancer prognosis can, in fact, be expressed through CT scans. |
format | Online Article Text |
id | pubmed-9689406 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96894062022-11-25 Radiomics in Head and Neck Cancer Outcome Predictions Gonçalves, Maria Gsaxner, Christina Ferreira, André Li, Jianning Puladi, Behrus Kleesiek, Jens Egger, Jan Alves, Victor Diagnostics (Basel) Article Head and neck cancer has great regional anatomical complexity, as it can develop in different structures, exhibiting diverse tumour manifestations and high intratumoural heterogeneity, which is highly related to resistance to treatment, progression, the appearance of metastases, and tumour recurrences. Radiomics has the potential to address these obstacles by extracting quantitative, measurable, and extractable features from the region of interest in medical images. Medical imaging is a common source of information in clinical practice, presenting a potential alternative to biopsy, as it allows the extraction of a large number of features that, although not visible to the naked eye, may be relevant for tumour characterisation. Taking advantage of machine learning techniques, the set of features extracted when associated with biological parameters can be used for diagnosis, prognosis, and predictive accuracy valuable for clinical decision-making. Therefore, the main goal of this contribution was to determine to what extent the features extracted from Computed Tomography (CT) are related to cancer prognosis, namely Locoregional Recurrences (LRs), the development of Distant Metastases (DMs), and Overall Survival (OS). Through the set of tumour characteristics, predictive models were developed using machine learning techniques. The tumour was described by radiomic features, extracted from images, and by the clinical data of the patient. The performance of the models demonstrated that the most successful algorithm was XGBoost, and the inclusion of the patients’ clinical data was an asset for cancer prognosis. Under these conditions, models were created that can reliably predict the LR, DM, and OS status, with the area under the ROC curve (AUC) values equal to 0.74, 0.84, and 0.91, respectively. In summary, the promising results obtained show the potential of radiomics, once the considered cancer prognosis can, in fact, be expressed through CT scans. MDPI 2022-11-08 /pmc/articles/PMC9689406/ /pubmed/36359576 http://dx.doi.org/10.3390/diagnostics12112733 Text en © 2022 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 | Article Gonçalves, Maria Gsaxner, Christina Ferreira, André Li, Jianning Puladi, Behrus Kleesiek, Jens Egger, Jan Alves, Victor Radiomics in Head and Neck Cancer Outcome Predictions |
title | Radiomics in Head and Neck Cancer Outcome Predictions |
title_full | Radiomics in Head and Neck Cancer Outcome Predictions |
title_fullStr | Radiomics in Head and Neck Cancer Outcome Predictions |
title_full_unstemmed | Radiomics in Head and Neck Cancer Outcome Predictions |
title_short | Radiomics in Head and Neck Cancer Outcome Predictions |
title_sort | radiomics in head and neck cancer outcome predictions |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9689406/ https://www.ncbi.nlm.nih.gov/pubmed/36359576 http://dx.doi.org/10.3390/diagnostics12112733 |
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