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

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-[18F]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-[18F]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.

• Authors: Sebastian N Marschner, Elia Lombardo, Lena Minibek, Adrien Holzgreve, Lena Kaiser, Nathalie L Albert, Christopher Kurz, Marco Riboldi, Richard Späth, Philipp Baumeister, Maximilian Niyazi, Claus Belka, Stefanie Corradini, Guillaume Landry, Franziska Walter
• Journal: Diagnostics (Basel, Switzerland) (Diagnostics (Basel)) Vol. 11 Issue 9 (Aug 31 2021) ISSN: 2075-4418 [Print] Switzerland
• PMID: 34573924 (Publication Type: Journal Article)