Objective: The aim of the study was to evaluate the CT and fluorine-18-fluorodeoxyglucose positron emission tomography/CT (18F-FDG PET/CT) imaging findings of lung mucosa associated lymphoid tissue (MALT) lymphoma. Methods: 28 patients with histologically confirmed pulmonary MALT lymphoma who underwent a chest CT and 18F-FDG PET/CT for staging were retrospectively analysed. The CT images were evaluated to determine morphological pattern of appearance, laterality, localization, number, size, presence of thoracic lymphadenopaties and secondary/combined findings. PET images were analysed visually and semi-quantitatively by measuring the maximum standardized uptake value (SUVmax), lesion-to-liver SUVmax ratio and lesion-to-blood pool SUVmax ratio. The relationship between qualitative and semi-quantitative features at 18F-FDG PET/CT and CT findings were also analysed. Results: A total of 57 pulmonary lesions were identified by CT: 37 areas of consolidation, 4 masses, 12 nodules and 4 ground-glass opacities. Solitary and multiple lesions were detected in 10 and 18 patients, respectively; among patients with multiple lesions, 16 were bilateral and 2 unilateral. 18F-FDG PET/CT revealed increased 18F-FDG uptake in 47/57 lesions, in 26/28 patients. 18F-FDG avidity was significantly associated only with tumour size. Conclusions: Pulmonary MALT lymphoma is 18F-FDG avid in most cases and 18F-FDG avidity is correlated with tumour size. Consolidation is the most frequent morphological pattern of disease presentation. Advances in knowledge: This study demonstrated that lung MALT lymphoma are 18F-FDG avid in most cases depending on tumour size. Single or multiple areas of consolidation are the most common pattern of presentation of lung MALT lymphoma at CT.

The aim of this retrospective study was to investigate the ability of 18 fluorine-fluorodeoxyglucose positron emission tomography/CT (¹⁸F-FDG-PET/CT) metrics and radiomics features (RFs) in predicting the final diagnosis of solitary pulmonary nodules (SPN). We retrospectively recruited 202 patients who underwent a ¹⁸F-FDG-PET/CT before any treatment in two PET scanners. After volumetric segmentation of each lung nodule, 8 PET metrics and 42 RFs were extracted. All the features were tested for significant differences between the two PET scanners. The performances of all features in predicting the nature of SPN were analyzed by testing three classes of final logistic regression predictive models: two were built/trained through exploiting the separate data from the two scanners, and the other joined the data together. One hundred and twenty-seven patients had a final diagnosis of malignancy, while 64 were of a benign nature. Comparing the two PET scanners, we found that all metabolic features and most of RFs were significantly different, despite the cross correlation being quite similar. For scanner 1, a combination between grey level co-occurrence matrix (GLCM), histogram, and grey-level zone length matrix (GLZLM) related features presented the best performances to predict the diagnosis; for scanner 2, it was GLCM and histogram-related features and metabolic tumour volume (MTV); and for scanner 1 + 2, it was histogram features, standardized uptake value (SUV) metrics, and MTV. RFs had a significant role in predicting the diagnosis of SPN, but their accuracies were directly related to the scanner.