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Influence of Semiquantitative [(18)F]FDG PET and Hematological Parameters on Survival in HNSCC Patients Using Neural Network Analysis

The aim of this study is to assess the influence of semiquantitative PET-derived parameters as well as hematological parameters in overall survival in HNSCC patients using neural network analysis. Retrospective analysis was performed on 106 previously untreated HNSCC patients. Several PET-derived pa...

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Autores principales: Cegla, Paulina, Currie, Geoffrey, Wróblewska, Joanna P., Cholewiński, Witold, Kaźmierska, Joanna, Marszałek, Andrzej, Kubiak, Anna, Golusinski, Pawel, Golusiński, Wojciech, Majchrzak, Ewa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8875232/
https://www.ncbi.nlm.nih.gov/pubmed/35215335
http://dx.doi.org/10.3390/ph15020224
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author Cegla, Paulina
Currie, Geoffrey
Wróblewska, Joanna P.
Cholewiński, Witold
Kaźmierska, Joanna
Marszałek, Andrzej
Kubiak, Anna
Golusinski, Pawel
Golusiński, Wojciech
Majchrzak, Ewa
author_facet Cegla, Paulina
Currie, Geoffrey
Wróblewska, Joanna P.
Cholewiński, Witold
Kaźmierska, Joanna
Marszałek, Andrzej
Kubiak, Anna
Golusinski, Pawel
Golusiński, Wojciech
Majchrzak, Ewa
author_sort Cegla, Paulina
collection PubMed
description The aim of this study is to assess the influence of semiquantitative PET-derived parameters as well as hematological parameters in overall survival in HNSCC patients using neural network analysis. Retrospective analysis was performed on 106 previously untreated HNSCC patients. Several PET-derived parameters (SUV(max), SUV(mean), TotalSUV, MTV, TLG, TLR(max), TLR(mean), TLR(TLG), and HI) for primary tumor and lymph node with highest activity were assessed. Additionally, hematological parameters (LEU, LEU%, NEU, NEU%, MON, MON%, PLT, PLT%, NRL, and LMR) were also assessed. Patients were divided according to the diagnosis into the good and bad group. The data were evaluated using an artificial neural network (Neural Analyzer version 2.9.5) and conventional statistic. Statistically significant differences in PET-derived parameters in 5-year survival rate between group of patients with worse prognosis and good prognosis were shown in primary tumor SUV(max) (10.0 vs. 7.7; p = 0.040), SUV(mean) (5.4 vs. 4.4; p = 0.047), MTV (23.2 vs. 14.5; p = 0.010), and TLG (155.0 vs. 87.5; p = 0.05), and mean liver TLG (27.8 vs. 30.4; p = 0.031), TLR(max) (3.8 vs. 2.6; p = 0.019), TLR(mean) (2.8 vs. 1.9; p = 0.018), and in TLR(TLG) (5.6 vs. 2.3; p = 0.042). From hematological parameters, only LMR showed significant differences (2.5 vs. 3.2; p = 0.009). Final neural network showed that for ages above 60, primary tumors SUV(max), TotalSUV, MTV, TLG, TLR(max), and TLR(mean) over (9.7, 2255, 20.6, 145, 3.6, 2.6, respectively) are associated with worse survival. Our study shows that the neural network could serve as a supplement to PET-derived parameters and is helpful in finding prognostic parameters for overall survival in HNSCC.
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spelling pubmed-88752322022-02-26 Influence of Semiquantitative [(18)F]FDG PET and Hematological Parameters on Survival in HNSCC Patients Using Neural Network Analysis Cegla, Paulina Currie, Geoffrey Wróblewska, Joanna P. Cholewiński, Witold Kaźmierska, Joanna Marszałek, Andrzej Kubiak, Anna Golusinski, Pawel Golusiński, Wojciech Majchrzak, Ewa Pharmaceuticals (Basel) Article The aim of this study is to assess the influence of semiquantitative PET-derived parameters as well as hematological parameters in overall survival in HNSCC patients using neural network analysis. Retrospective analysis was performed on 106 previously untreated HNSCC patients. Several PET-derived parameters (SUV(max), SUV(mean), TotalSUV, MTV, TLG, TLR(max), TLR(mean), TLR(TLG), and HI) for primary tumor and lymph node with highest activity were assessed. Additionally, hematological parameters (LEU, LEU%, NEU, NEU%, MON, MON%, PLT, PLT%, NRL, and LMR) were also assessed. Patients were divided according to the diagnosis into the good and bad group. The data were evaluated using an artificial neural network (Neural Analyzer version 2.9.5) and conventional statistic. Statistically significant differences in PET-derived parameters in 5-year survival rate between group of patients with worse prognosis and good prognosis were shown in primary tumor SUV(max) (10.0 vs. 7.7; p = 0.040), SUV(mean) (5.4 vs. 4.4; p = 0.047), MTV (23.2 vs. 14.5; p = 0.010), and TLG (155.0 vs. 87.5; p = 0.05), and mean liver TLG (27.8 vs. 30.4; p = 0.031), TLR(max) (3.8 vs. 2.6; p = 0.019), TLR(mean) (2.8 vs. 1.9; p = 0.018), and in TLR(TLG) (5.6 vs. 2.3; p = 0.042). From hematological parameters, only LMR showed significant differences (2.5 vs. 3.2; p = 0.009). Final neural network showed that for ages above 60, primary tumors SUV(max), TotalSUV, MTV, TLG, TLR(max), and TLR(mean) over (9.7, 2255, 20.6, 145, 3.6, 2.6, respectively) are associated with worse survival. Our study shows that the neural network could serve as a supplement to PET-derived parameters and is helpful in finding prognostic parameters for overall survival in HNSCC. MDPI 2022-02-14 /pmc/articles/PMC8875232/ /pubmed/35215335 http://dx.doi.org/10.3390/ph15020224 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
Cegla, Paulina
Currie, Geoffrey
Wróblewska, Joanna P.
Cholewiński, Witold
Kaźmierska, Joanna
Marszałek, Andrzej
Kubiak, Anna
Golusinski, Pawel
Golusiński, Wojciech
Majchrzak, Ewa
Influence of Semiquantitative [(18)F]FDG PET and Hematological Parameters on Survival in HNSCC Patients Using Neural Network Analysis
title Influence of Semiquantitative [(18)F]FDG PET and Hematological Parameters on Survival in HNSCC Patients Using Neural Network Analysis
title_full Influence of Semiquantitative [(18)F]FDG PET and Hematological Parameters on Survival in HNSCC Patients Using Neural Network Analysis
title_fullStr Influence of Semiquantitative [(18)F]FDG PET and Hematological Parameters on Survival in HNSCC Patients Using Neural Network Analysis
title_full_unstemmed Influence of Semiquantitative [(18)F]FDG PET and Hematological Parameters on Survival in HNSCC Patients Using Neural Network Analysis
title_short Influence of Semiquantitative [(18)F]FDG PET and Hematological Parameters on Survival in HNSCC Patients Using Neural Network Analysis
title_sort influence of semiquantitative [(18)f]fdg pet and hematological parameters on survival in hnscc patients using neural network analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8875232/
https://www.ncbi.nlm.nih.gov/pubmed/35215335
http://dx.doi.org/10.3390/ph15020224
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