The object of this study was to assess the similarities and differences of radiomics features on full field digital mammography (FFDM) in FOR PROCESSING and FOR PRESENTATION data. The authors aimed to address the problem using an enlarged set of texture radiomic features, dense/non-dense areas comparison and a new manufacturer, concluding that texture features from FOR PROCESSING mammograms were the most suitable for assessing breast density. Key points Segmentation from FOR PROCESSING and FOR PRESENTATION gave very different results. Bilateral symmetry was higher when evaluated on features computed using FOR PROCESSING images. Texture features from FOR PROCESSING mammograms seem to be most suitable for assessing breast density. Article: Radiomic features of breast parenchyma: assessing differences between FOR PROCESSING and FOR PRESENTATION digital mammography Authors: Mario Sansone, Roberta Grassi, Maria Paola Belfiore, Gianluca Gatta, Francesca Grassi, Fabio Pinto, Giorgia Viola La Casella, Roberta Fusco, Salvatore Cappabianca, Vincenza Granata & Roberto Grassi

Impact of deep learning reconstruction on radiation dose reduction and cancer risk in CT examinations
Deep‑learning reconstruction (DLR) shifts CT image formation from a hardware‑limited process to a data‑driven one. In our real‑world cohort of >10,000 body scans, we observed a

