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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...
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/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. |
format | Online Article Text |
id | pubmed-8875232 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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