Purpose To assess the influence of long-axial field-of-view (LAFOV) PET/CT systems on radiomics feature reliability, to assess the suitability for short-duration or low-activity acquisitions for textural feature analysis and to investigate the influence of acceptance angle. Methods 34 patients were analysed: twelve patients underwent oncological 2-[18F]-FDG PET/CT, fourteen [18F]PSMA-1007 and eight [68Ga]Ga-DOTATOC. Data were obtained using a 106 cm LAFOV system for 10 min. Sinograms were generated from list-mode data corresponding to scan durations of 2, 5, 10, 20, 30, 60, 120, 240, 360 and 600s using both standard (minimum ring difference MRD 85 crystals) and maximum acceptance angles (MRD 322). Target lesions were segmented and radiomics features were calculated. To assess feature correlation, Pearson's product-moment correlation coefficient (PPMCC) was calculated with respect to the full duration acquisition for MRD 85 and 322 respectively. The number of features with excellent (r > 0.9), moderate (r > 0.7 and < 0.9) and poor (r ≤ 0.7) correlation was compared as a measure of feature stability. Intra-class heterogeneity was assessed by means of the quartile coefficient of dispersion. Results As expected, PPMCC improved with acquisition time for all features. By 240s almost all features showed at least moderate agreement with the full count (C100%) data, and by 360s almost all showed excellent agreement. Compared to standard-axial field of view (SAFOV) equivalent scans, fewer features showed moderate or poor agreement, and this was most pronounced for [68Ga]Ga-DOTATOC. Data obtained at C100% at MRD 322 were better able to capture between-patient heterogeneities. Conclusion The improved feature reliability at longer acquisition times and higher MRD demonstrate the advantages of high sensitivity LAFOV systems for reproducible and low-noise data. High fidelity between MRD 85 and MRD 322 was seen at all scan durations > 2s. When contrasted with data comparable to a simulated SAFOV acquisition, full-count and full-MRD data were better able to capture underlying feature heterogeneities.
