BACKGROUND: The aim of this study is to determine if the choice of the 18F-FDG-PET protocol, especially matrix size and reconstruction algorithm, is of importance to discriminate between immunohistochemical subtypes (luminal versus non-luminal) in breast cancer with textural features (TFs). PROCEDURES: Forty-seven patients referred for breast cancer staging in the framework of a prospective study were reviewed as part of an ancillary study. In addition to standard PET imaging (PSFWholeBody), a high-resolution breast acquisition was performed and reconstructed with OSEM and PSF (OSEMbreast/PSFbreast). PET standard metrics and TFs were extracted. For each reconstruction protocol, a prediction model for tumour classification was built using a random forests method. Spearman coefficients were used to seek correlation between PET metrics. RESULTS: PSFWholeBody showed lower numbers of voxels within VOIs than OSEMbreast and PSFbreast with median (interquartile range) equal to 130 (43-271), 316 (167-1042), 367 (107-1221), respectively (p < 0.0001). Therefore, using LifeX software, 28 (59%), 46 (98%) and 42 (89%) patients were exploitable with PSFWholeBody, OSEMbreast and PSFbreast, respectively. On matched comparisons, PSFbreast reconstruction presented better abilities than PSFwholeBody and OSEMbreast for the classification of luminal versus non-luminal breast tumours with an accuracy reaching 85.7% as compared to 67.8% for PSFwholeBody and 73.8% for OSEMbreast. PSFbreast accuracy, sensitivity, specificity, PPV and NPV were equal to 85.7%, 94.3%, 42.9%, 89.2%, 60.0%, respectively. Coarseness and ZLNU were found to be main variables of importance, appearing in all three prediction models. Coarseness was correlated with SUVmax on PSFwholeBody images (ρ = - 0.526, p = 0.005), whereas it was not on OSEMbreast (ρ = - 0.183, p = 0.244) and PSFbreast (ρ = - 0.244, p = 0.119) images. Moreover, the range of its values was higher on PSFbreast images as compared to OSEMbreast, especially in small lesions (MTV < 3 ml). CONCLUSIONS: High-resolution breast PET acquisitions, applying both small-voxel matrix and PSF modelling, appeared to improve the characterisation of breast tumours.