TY - JOUR
T1 - The influence of semiquantitative [18F]FDG PET and hematological parameters on survival in HNSCC patients using neural network analysis
AU - Currie, Geoff
PY - 2022
Y1 - 2022
N2 - Background: 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. Material and Methods: Retrospective analysis was performed on
106 previously untreated HNSCC patients. Several PET-derived parameters
(SUVmax, SUVmean, TotalSUV, MTV, TLG, TLRmax, TLRmean, TLRTLG,
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. Results: 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 SUVmax (10.0 vs. 7.7; p = 0.040), SUVmean (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), TLRmax (3.8 vs. 2.6; p = 0.019), TLRmean (2.8 vs. 1.9; p = 0.018), and in TLRTLG (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 SUVmax, TotalSUV, MTV, TLG, TLRmax, and TLRmean
over (9.7, 2255, 20.6, 145, 3.6, 2.6, respectively) are associated with
worse survival. Conclusion: 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.
AB - Background: 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. Material and Methods: Retrospective analysis was performed on
106 previously untreated HNSCC patients. Several PET-derived parameters
(SUVmax, SUVmean, TotalSUV, MTV, TLG, TLRmax, TLRmean, TLRTLG,
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. Results: 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 SUVmax (10.0 vs. 7.7; p = 0.040), SUVmean (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), TLRmax (3.8 vs. 2.6; p = 0.019), TLRmean (2.8 vs. 1.9; p = 0.018), and in TLRTLG (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 SUVmax, TotalSUV, MTV, TLG, TLRmax, and TLRmean
over (9.7, 2255, 20.6, 145, 3.6, 2.6, respectively) are associated with
worse survival. Conclusion: 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.
KW - positron emission tomography/computed tomography
KW - head and neck swuamous cell carcinoma
KW - overall survival
KW - neural network
UR - https://www.mdpi.com/1424-8247/15/2/224/pdf
U2 - https://doi.org/10.3390/ph15020224
DO - https://doi.org/10.3390/ph15020224
M3 - Article
VL - 15
JO - Pharmaceuticals
JF - Pharmaceuticals
SN - 1424-8247
IS - 2
M1 - 224
ER -