18F-FDG PET/CT heterogeneity quantification through textural features in the era of harmonisation programs: a focus on lung cancer.
OBJECTIVE
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 selected18F-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 PSF7and OSEM ones, and with a 50 % standardised uptake values (SUV)maxthreshold (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 PSF7images. 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 PSF7reconstructions. Furthermore, the distributions of TF for OSEM and PSF7reconstructions according to tumour volumes were similar for all ranges of volumes.


CONCLUSIONS
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.
Nuclear Medicine Department, University Hospital, Caen, France.|Biologie et Thérapies Innovantes des Cancers Localement Agressifs, Université de Caen Normandie, INSERM, Caen, France.|Normandie University, Caen, France.
Charline
C
Lasnon
LaTIM, INSERM UMR 1101, Brest, France.
Mohamed
M
Majdoub
LaTIM, INSERM UMR 1101, Brest, France.
Brice
B
Lavigne
Thoracic Oncology, François Baclesse Cancer Centre, Caen, France.
Pascal
P
Do
Pulmonology Department, Caen University Hospital, Caen, France.
Jeannick
J
Madelaine
LaTIM, INSERM UMR 1101, Brest, France.
Dimitris
D
Visvikis
LaTIM, INSERM UMR 1101, Brest, France. mathieu.hatt@inserm.fr.|CHRU Morvan, INSERM UMR 1101, Laboratoire de Traitement de l'Information Medicale (LaTIM), Groupe 'Imagerie multi-modalité quantitative pour le diagnostic et la thérapie', Bâtiment 1, 1er étage, bureau 1056, 2 avenue Foch, 29609, Brest, France. mathieu.hatt@inserm.fr.
Mathieu
M
Hatt
Nuclear Medicine Department, University Hospital, Caen, France. aide-n@chu-caen.fr.|Biologie et Thérapies Innovantes des Cancers Localement Agressifs, Université de Caen Normandie, INSERM, Caen, France. aide-n@chu-caen.fr.|Normandie University, Caen, France. aide-n@chu-caen.fr.|Nuclear Medicine Department, Caen University Hospital, Avenue Côte de Nacre, 14000, Caen, France. aide-n@chu-caen.fr.
Nicolas
http://orcid.org/0000-0001-9207-0847
N
Aide
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D005060:Europe; D005260:Female; D019788:Fluorodeoxyglucose F18; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000592:standards*; D021621:Imaging, Three-Dimensional / Q000592:standards; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000592:standards*; D000072078:Positron Emission Tomography Computed Tomography / Q000592:standards*; D017410:Practice Guidelines as Topic; D019275:Radiopharmaceuticals; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D047368:Tumor Burden
D019275:Radiopharmaceuticals; D019788:Fluorodeoxyglucose F18
EARL accreditation program; FDG PET/CT; Harmonisation; Heterogeneity; Lung cancer; Quantification