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Risk Stratification Using (18)F-FDG PET/CT and Artificial Neural Networks in Head and Neck Cancer Patients Undergoing Radiotherapy

This study retrospectively analyzed the performance of artificial neural networks (ANN) to predict overall survival (OS) or locoregional failure (LRF) in HNSCC patients undergoing radiotherapy, based on 2-[(18)F]FDG PET/CT and clinical covariates. We compared predictions relying on three different s...

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Autores principales: Marschner, Sebastian N., Lombardo, Elia, Minibek, Lena, Holzgreve, Adrien, Kaiser, Lena, Albert, Nathalie L., Kurz, Christopher, Riboldi, Marco, Späth, Richard, Baumeister, Philipp, Niyazi, Maximilian, Belka, Claus, Corradini, Stefanie, Landry, Guillaume, Walter, Franziska
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8468242/
https://www.ncbi.nlm.nih.gov/pubmed/34573924
http://dx.doi.org/10.3390/diagnostics11091581
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author Marschner, Sebastian N.
Lombardo, Elia
Minibek, Lena
Holzgreve, Adrien
Kaiser, Lena
Albert, Nathalie L.
Kurz, Christopher
Riboldi, Marco
Späth, Richard
Baumeister, Philipp
Niyazi, Maximilian
Belka, Claus
Corradini, Stefanie
Landry, Guillaume
Walter, Franziska
author_facet Marschner, Sebastian N.
Lombardo, Elia
Minibek, Lena
Holzgreve, Adrien
Kaiser, Lena
Albert, Nathalie L.
Kurz, Christopher
Riboldi, Marco
Späth, Richard
Baumeister, Philipp
Niyazi, Maximilian
Belka, Claus
Corradini, Stefanie
Landry, Guillaume
Walter, Franziska
author_sort Marschner, Sebastian N.
collection PubMed
description This study retrospectively analyzed the performance of artificial neural networks (ANN) to predict overall survival (OS) or locoregional failure (LRF) in HNSCC patients undergoing radiotherapy, based on 2-[(18)F]FDG PET/CT and clinical covariates. We compared predictions relying on three different sets of features, extracted from 230 patients. Specifically, (i) an automated feature selection method independent of expert rating was compared with (ii) clinical variables with proven influence on OS or LRF and (iii) clinical data plus expert-selected SUV metrics. The three sets were given as input to an artificial neural network for outcome prediction, evaluated by Harrell’s concordance index (HCI) and by testing stratification capability. For OS and LRF, the best performance was achieved with expert-based PET-features (0.71 HCI) and clinical variables (0.70 HCI), respectively. For OS stratification, all three feature sets were significant, whereas for LRF only expert-based PET-features successfully classified low vs. high-risk patients. Based on 2-[(18)F]FDG PET/CT features, stratification into risk groups using ANN for OS and LRF is possible. Differences in the results for different feature sets confirm the relevance of feature selection, and the key importance of expert knowledge vs. automated selection.
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spelling pubmed-84682422021-09-27 Risk Stratification Using (18)F-FDG PET/CT and Artificial Neural Networks in Head and Neck Cancer Patients Undergoing Radiotherapy Marschner, Sebastian N. Lombardo, Elia Minibek, Lena Holzgreve, Adrien Kaiser, Lena Albert, Nathalie L. Kurz, Christopher Riboldi, Marco Späth, Richard Baumeister, Philipp Niyazi, Maximilian Belka, Claus Corradini, Stefanie Landry, Guillaume Walter, Franziska Diagnostics (Basel) Article This study retrospectively analyzed the performance of artificial neural networks (ANN) to predict overall survival (OS) or locoregional failure (LRF) in HNSCC patients undergoing radiotherapy, based on 2-[(18)F]FDG PET/CT and clinical covariates. We compared predictions relying on three different sets of features, extracted from 230 patients. Specifically, (i) an automated feature selection method independent of expert rating was compared with (ii) clinical variables with proven influence on OS or LRF and (iii) clinical data plus expert-selected SUV metrics. The three sets were given as input to an artificial neural network for outcome prediction, evaluated by Harrell’s concordance index (HCI) and by testing stratification capability. For OS and LRF, the best performance was achieved with expert-based PET-features (0.71 HCI) and clinical variables (0.70 HCI), respectively. For OS stratification, all three feature sets were significant, whereas for LRF only expert-based PET-features successfully classified low vs. high-risk patients. Based on 2-[(18)F]FDG PET/CT features, stratification into risk groups using ANN for OS and LRF is possible. Differences in the results for different feature sets confirm the relevance of feature selection, and the key importance of expert knowledge vs. automated selection. MDPI 2021-08-31 /pmc/articles/PMC8468242/ /pubmed/34573924 http://dx.doi.org/10.3390/diagnostics11091581 Text en © 2021 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
Marschner, Sebastian N.
Lombardo, Elia
Minibek, Lena
Holzgreve, Adrien
Kaiser, Lena
Albert, Nathalie L.
Kurz, Christopher
Riboldi, Marco
Späth, Richard
Baumeister, Philipp
Niyazi, Maximilian
Belka, Claus
Corradini, Stefanie
Landry, Guillaume
Walter, Franziska
Risk Stratification Using (18)F-FDG PET/CT and Artificial Neural Networks in Head and Neck Cancer Patients Undergoing Radiotherapy
title Risk Stratification Using (18)F-FDG PET/CT and Artificial Neural Networks in Head and Neck Cancer Patients Undergoing Radiotherapy
title_full Risk Stratification Using (18)F-FDG PET/CT and Artificial Neural Networks in Head and Neck Cancer Patients Undergoing Radiotherapy
title_fullStr Risk Stratification Using (18)F-FDG PET/CT and Artificial Neural Networks in Head and Neck Cancer Patients Undergoing Radiotherapy
title_full_unstemmed Risk Stratification Using (18)F-FDG PET/CT and Artificial Neural Networks in Head and Neck Cancer Patients Undergoing Radiotherapy
title_short Risk Stratification Using (18)F-FDG PET/CT and Artificial Neural Networks in Head and Neck Cancer Patients Undergoing Radiotherapy
title_sort risk stratification using (18)f-fdg pet/ct and artificial neural networks in head and neck cancer patients undergoing radiotherapy
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8468242/
https://www.ncbi.nlm.nih.gov/pubmed/34573924
http://dx.doi.org/10.3390/diagnostics11091581
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