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Purpose To elucidate the underlying lung changes responsible for the computed tomographic (CT) features of idiopathic pulmonary fibrosis (IPF) and to gain insight into the way IPF proceeds through the lungs and progresses over time. Materials and Methods Micro-CT studies of tissue cores obtained from explant lungs were examined and were correlated 1:1 with a CT study obtained immediately before transplantation. Samples for histologic analysis were obtained from selected cores. Results In areas with no or minimal abnormalities on CT images, small areas of increased attenuation located in or near the interlobular septa can be seen on micro-CT studies. In more involved lung areas, the number of opacities increases and opacities enlarge and approach each other along the interlobular septa, causing a fine reticular pattern on CT images. Simultaneously, air-containing structures in and around these opacities arise, corresponding with small cysts on CT images. Honeycombing is caused by a progressive increase in the number and size of these cystic structures and tissue opacities that gradually extend toward the centrilobular region and finally replace the entire lobule. At histologic analysis, the small islands of increased attenuation very likely correspond with fibroblastic foci. Near these fibroblastic foci, an abnormal adjacency of alveolar walls was seen, suggesting alveolar collapse. In later stages, normal lung tissue is replaced by a large amount of young collagen, as seen in patients with advanced fibrosis. Conclusion Fibrosis and cyst formation in patients with IPF seem to start at the periphery of the pulmonary lobule and progressively extend toward the core of this anatomic lung unit. Evidence was found that alveolar collapse might already be present in an early stage when there is only little pulmonary fibrosis. RSNA, 2016.

Extensive changes of dilatation and obstruction in nearly all airway generations were observed in end-stage CF lung disease.

Background Detection of the site of recurrence using PSMA-PET/CT is important to guide treatment in patients with biochemical recurrence of prostate cancer (PCa). The aim of this study was to evaluate the positivity rate of [18F]PSMA-1007-PET/CT in patients with biochemically recurrent PCa and identify parameters that predict scan positivity as well as the type and number of detected lesions. This monocentric retrospective study included 137 PCa patients with biochemical recurrence who underwent one or more [18F]PSMA-1007-PET/CT scans between August 2018 and June 2019. PET-positive malignant lesions were classified as local recurrence, lymph node (LN), bone or soft tissue lesions. The association between biochemical/paraclinical parameters, as PSA value, PSA doubling time, PSA velocity, Gleason score (GS) and androgen deprivation therapy (ADT), and scan positivity as well as type and number of detected lesions was evaluated using logistic regression analysis (binary outcomes) and Poisson models (count-type outcomes). Results We included 175 [18F]PSMA-1007-PET/CT scans after radical prostatectomy (78%), external beam radiation therapy (8.8%), ADT (7.3%), brachytherapy (5.1%) and high intensity focused ultrasound (0.7%) as primary treatment (median PSA value 1.6 ng/ml). Positivity rate was 80%. PSA value and PSA velocity were significant predictors of scan positivity as well as of the presence of bone and soft tissue lesions and number of bone, LN and soft tissue lesions, both in uni- and/or multivariable analysis. Multivariable analysis also showed prior ADT as predictor of bone and soft tissue lesions, GS as predictor of the number of bone lesions and ongoing ADT as predictor of the number of LN lesions. Conclusion [18F]PSMA-1007-PET/CT showed a high positivity rate in patients with biochemically recurrent PCa. PSA value and PSA velocity were significant predictors of scan positivity as well as of the presence and number of bone and soft tissue lesions and the number of LN lesions. Our findings can guide clinicians in optimal patient selection for [18F]PSMA-1007-PET/CT and support further research leading to the development of a prediction nomogram.