TY - JOUR
T1 - [18F]fluorodeoxyglucose positron emission tomography/computed tomography in combination with clinical data in predicting overall survival in non-small-cell lung cancer patients
T2 - A retrospective study
AU - Cegla, P.
AU - Currie, G. M.
AU - Cholewinski, W.
AU - Bryl, M.
AU - Trojanowski, M.
AU - Matuszewski, K.
AU - Piotrowski, T.
AU - Czepczyński, R.
N1 - Publisher Copyright:
© 2024
PY - 2024/5
Y1 - 2024/5
N2 - Introduction: Positron emission tomography/computed tomography (PET/CT) has an established role in evaluating patients with lung cancer. The aim of this work was to assess the predictive capability of [18F]Fluorodeoxyglucose ([18F]FDG) PET/CT parameters on overall survival (OS) in lung cancer patients using an artificial neural network (ANN) in parallel with conventional statistical analysis. Methods: Retrospective analysis was performed on a group of 165 lung cancer patients (98M, 67F). PET features associated with the primary tumor: maximum and mean standardized uptake value (SUVmax, SUVmean), total lesion glycolysis (TLG) metabolic tumor volume (MTV) and area under the curve-cumulative SUV histogram (AUC-CSH) and metastatic lesions (SUVmaxtotal, SUVmeantotal, TLGtotal, and MTVtotal) were evaluated. In parallel with conventional statistical analysis (Chi-Square analysis for nominal data, Student's t test for continuous data), the data was evaluated using an ANN. There were 97 input variables in 165 patients using a binary classification of either below, or greater than/equal to median survival post primary diagnosis. Additionally, phantom study was performed to assess the most optimal contouring method. Results: Males had statistically higher SUVmax (mean: 10.7 vs 8.9; p = 0.020), MTV (mean: 66.5 cm3 vs. 21.5 cm3; p = 0.001), TLG (mean 404.7 vs. 115.0; p = 0.003), TLGtotal (mean: 946.7 vs. 433.3; p = 0.014) and MTVtotal (mean: 242.0 cm3 vs. 103.7 cm3; p = 0.027) than females. The ANN after training and validation was optimised with a final architecture of 4 scaling layer inputs (TLGtotal, SUVmaxtotal, SUVmeantotal and disease stage) and receiving operator characteristic (ROC) analysis demonstrated an AUC of 0.764 (sensitivity of 92.3%, specificity of 57.1%). Conclusion: Conventional statistical analysis and the ANN provided concordant findings in relation to variables that predict decreased survival. The ANN provided a weighted algorithm of the 4 key features to predict decreased survival. Implication for practice: Identification of parameters which can predict survival in lung cancer patients might be helpful in choosing the group of patients who require closer look during the follow-up.
AB - Introduction: Positron emission tomography/computed tomography (PET/CT) has an established role in evaluating patients with lung cancer. The aim of this work was to assess the predictive capability of [18F]Fluorodeoxyglucose ([18F]FDG) PET/CT parameters on overall survival (OS) in lung cancer patients using an artificial neural network (ANN) in parallel with conventional statistical analysis. Methods: Retrospective analysis was performed on a group of 165 lung cancer patients (98M, 67F). PET features associated with the primary tumor: maximum and mean standardized uptake value (SUVmax, SUVmean), total lesion glycolysis (TLG) metabolic tumor volume (MTV) and area under the curve-cumulative SUV histogram (AUC-CSH) and metastatic lesions (SUVmaxtotal, SUVmeantotal, TLGtotal, and MTVtotal) were evaluated. In parallel with conventional statistical analysis (Chi-Square analysis for nominal data, Student's t test for continuous data), the data was evaluated using an ANN. There were 97 input variables in 165 patients using a binary classification of either below, or greater than/equal to median survival post primary diagnosis. Additionally, phantom study was performed to assess the most optimal contouring method. Results: Males had statistically higher SUVmax (mean: 10.7 vs 8.9; p = 0.020), MTV (mean: 66.5 cm3 vs. 21.5 cm3; p = 0.001), TLG (mean 404.7 vs. 115.0; p = 0.003), TLGtotal (mean: 946.7 vs. 433.3; p = 0.014) and MTVtotal (mean: 242.0 cm3 vs. 103.7 cm3; p = 0.027) than females. The ANN after training and validation was optimised with a final architecture of 4 scaling layer inputs (TLGtotal, SUVmaxtotal, SUVmeantotal and disease stage) and receiving operator characteristic (ROC) analysis demonstrated an AUC of 0.764 (sensitivity of 92.3%, specificity of 57.1%). Conclusion: Conventional statistical analysis and the ANN provided concordant findings in relation to variables that predict decreased survival. The ANN provided a weighted algorithm of the 4 key features to predict decreased survival. Implication for practice: Identification of parameters which can predict survival in lung cancer patients might be helpful in choosing the group of patients who require closer look during the follow-up.
KW - Computed tomography
KW - Lung cancer
KW - Neural network
KW - Positron emission tomography
KW - Survival
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UR - http://www.scopus.com/inward/citedby.url?scp=85190961222&partnerID=8YFLogxK
U2 - 10.1016/j.radi.2024.04.004
DO - 10.1016/j.radi.2024.04.004
M3 - Article
C2 - 38663216
AN - SCOPUS:85190961222
SN - 1078-8174
VL - 30
SP - 971
EP - 977
JO - Radiography
JF - Radiography
IS - 3
ER -