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

Authors :
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
Source :
Diagnostics (2075-4418); Sep2021, Vol. 11 Issue 9, p1581, 1p
Publication Year :
2021

Abstract

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-[<superscript>18</superscript>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-[<superscript>18</superscript>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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20754418
Volume :
11
Issue :
9
Database :
Complementary Index
Journal :
Diagnostics (2075-4418)
Publication Type :
Academic Journal
Accession number :
152688070
Full Text :
https://doi.org/10.3390/diagnostics11091581