Nicolas Aide, Pascal Do, Brice Lavigne, Jeannick Madelaine, Mathieu Hatt, Charline Lasnon, Mohamed Majdoub, Dimitris Visvikis, Biologie et Thérapies Innovantes des Cancers Localement Agressifs ( BioTICLA ), Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Centre Régional de Lutte contre le Cancer François Baclesse ( CRLC François Baclesse ) -Université de Caen Normandie ( UNICAEN ), Normandie Université ( NU ) -Normandie Université ( NU ), Imagerie et Stratégies Thérapeutiques des pathologies Cérébrales et Tumorales ( ISTCT ), Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Centre National de la Recherche Scientifique ( CNRS ) -Université de Caen Normandie ( UNICAEN ), Nuclear Medicine Department, University Hospital, Caen, Laboratoire de Traitement de l'Information Medicale ( LaTIM ), Université européenne de Bretagne ( UEB ) -Télécom Bretagne-Centre Hospitalier Régional Universitaire de Brest ( CHRU Brest ) -Université de Brest ( UBO ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Institut Mines-Télécom [Paris], Centre François Baclesse, Pulmonology department, University Hospital, Caen, Biologie et Thérapies Innovantes des Cancers Localement Agressifs (BioTICLA), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Régional de Lutte contre le Cancer François Baclesse [Caen] (UNICANCER/CRLC), UNICANCER-Tumorothèque de Caen Basse-Normandie (TCBN)-Normandie Université (NU)-UNICANCER-Tumorothèque de Caen Basse-Normandie (TCBN)-Normandie Université (NU)-Université de Caen Normandie (UNICAEN), Normandie Université (NU), Service de médecine nucléaire [CHU Caen], Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-CHU Caen, Normandie Université (NU)-Tumorothèque de Caen Basse-Normandie (TCBN)-Tumorothèque de Caen Basse-Normandie (TCBN), Laboratoire de Traitement de l'Information Medicale (LaTIM), Université européenne de Bretagne - European University of Brittany (UEB)-Télécom Bretagne-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Mines-Télécom [Paris] (IMT), Centre Régional de Lutte contre le Cancer François Baclesse [Caen] (UNICANCER/CRLC), UNICANCER-Tumorothèque de Caen Basse-Normandie (TCBN)-Normandie Université (NU), Service de pneumologie [CHU Caen], Normandie Université (NU)-Normandie Université (NU)-Centre Régional de Lutte contre le Cancer François Baclesse [Caen] (UNICANCER/CRLC), UNICANCER-Tumorothèque de Caen Basse-Normandie (TCBN)-Normandie Université (NU)-UNICANCER-Tumorothèque de Caen Basse-Normandie (TCBN)-Institut National de la Santé et de la Recherche Médicale (INSERM), Hatt, Mathieu, Normandie Université (NU)-UNICANCER-Tumorothèque de Caen Basse-Normandie (TCBN)-UNICANCER-Tumorothèque de Caen Basse-Normandie (TCBN)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université européenne de Bretagne - European University of Brittany (UEB)-Université de Brest (UBO)-Télécom Bretagne-Institut Mines-Télécom [Paris] (IMT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest), and Normandie Université (NU)-UNICANCER-Tumorothèque de Caen Basse-Normandie (TCBN)
International audience; PURPOSE:Quantification of tumour heterogeneity in PET images has recently gained interest, but has been shown to be dependent on image reconstruction. This study aimed to evaluate the impact of the EANM/EARL accreditation program on selected 18F-FDG heterogeneity metrics.METHODS:To carry out our study, we prospectively analysed 71 tumours in 60 biopsy-proven lung cancer patient acquisitions reconstructed with unfiltered point spread function (PSF) positron emission tomography (PET) images (optimised for diagnostic purposes), PSF-reconstructed images with a 7-mm Gaussian filter (PSF7) chosen to meet European Association of Nuclear Medicine (EANM) 1.0 harmonising standards, and EANM Research Ltd. (EARL)-compliant ordered subset expectation maximisation (OSEM) images. Delineation was performed with fuzzy locally adaptive Bayesian (FLAB) algorithm on PSF images and reported on PSF7 and OSEM ones, and with a 50 % standardised uptake values (SUV)max threshold (SUVmax50%) applied independently to each image. Robust and repeatable heterogeneity metrics including 1st-order [area under the curve of the cumulative histogram (CHAUC)], 2nd-order (entropy, correlation, and dissimilarity), and 3rd-order [high-intensity larger area emphasis (HILAE) and zone percentage (ZP)] textural features (TF) were statistically compared.RESULTS:Volumes obtained with SUVmax50% were significantly smaller than FLAB-derived ones, and were significantly smaller in PSF images compared to OSEM and PSF7 images. PSF-reconstructed images showed significantly higher SUVmax and SUVmean values, as well as heterogeneity for CHAUC, dissimilarity, correlation, and HILAE, and a wider range of heterogeneity values than OSEM images for most of the metrics considered, especially when analysing larger tumours. Histological subtypes had no impact on TF distribution. No significant difference was observed between any of the considered metrics (SUV or heterogeneity features) that we extracted from OSEM and PSF7 reconstructions. Furthermore, the distributions of TF for OSEM and PSF7 reconstructions according to tumour volumes were similar for all ranges of volumes.CONCLUSION:PSF reconstruction with Gaussian filtering chosen to meet harmonising standards resulted in similar SUV values and heterogeneity information as compared to OSEM images, which validates its use within the harmonisation strategy context. However, unfiltered PSF-reconstructed images also showed higher heterogeneity according to some metrics, as well as a wider range of heterogeneity values than OSEM images for most of the metrics considered, especially when analysing larger tumours. This suggests that, whenever available, unfiltered PSF images should also be exploited to obtain the most discriminative quantitative heterogeneity features.