The widespread use of imaging examinations has increased the detection of incidental adrenal lesions, which are mostly benign and non-functioning adenomas. The differentiation of a benign from a malignant adrenal mass can be crucial especially in oncology patients since it would greatly affect treatment and prognosis. In this setting, imaging plays a key role in the detection and characterization of adrenal lesions, with several imaging tools which can be employed by radiologists. A thorough knowledge of the imaging features of adrenal masses is essential to better characterize these lesions, avoiding a misinterpretation of imaging findings, which frequently overlap between benign and malignant conditions, thus helping clinicians and surgeons in the management of patients. The purpose of this paper is to provide an overview of the main imaging features of adrenal masses and tumor-like conditions recalling the strengths and weaknesses of imaging modalities commonly used in adrenal imaging.

Artificial intelligence (AI) transforms image data analysis across many biomedical fields, such as cell biology, radiology, pathology, cancer biology, and immunology, with object detection, image feature extraction, classification, and segmentation applications. Advancements in deep learning (DL) research have been a critical factor in advancing computer techniques for biomedical image analysis and data mining. A significant improvement in the accuracy of cell detection and segmentation algorithms has been achieved as a result of the emergence of open-source software and innovative deep neural network architectures. Automated cell segmentation now enables the extraction of quantifiable cellular and spatial features from microscope images of cells and tissues, providing critical insights into cellular organization in various diseases. This review aims to examine the latest AI and DL techniques for cell analysis and data mining in microscopy images, aid the biologists who have less background knowledge in AI and machine learning (ML), and incorporate the ML models into microscopy focus images.

Purpose The object of this study was to assess whether ¹⁸F-fluorodeoxyglucose PET/CT (FDG PET/CT) provides novel information in patients with Takayasu’s arteritis (TA) in addition to that provided by current activity assessment, to analyse the effects of possible confounders, such as arterial grafts, and to verify whether PET/CT could be informative in lesions <4 mm thick. Methods We studied 30 patients with TA, evaluated from October 2010 to April 2014 by both PET/CT and magnetic resonance imaging (MRI). All arterial lesions were evaluated by PET both qualitatively (positive/negative) and semiquantitatively (maximum standardized uptake value, SUV_max), and the thickness of lesions in the MRI field of view was evaluated. In a per-patient analysis, the relationships between the PET data and acute-phase reactants and NIH criteria for active TA were evaluated. In a per-lesion analysis, the relationships between the PET features of each lesion and MRI morphological data were evaluated. The effects of the presence of arterial grafts were also evaluated. Results Increased FDG uptake was seen in 16 of 30 patients (53%) and in 46 of 177 vascular lesions (26%). Significant periprosthetic FDG uptake was seen in 6 of 7 patients (86%) with previous vascular surgery and in 10 of 11 of grafts (91%). Graft-associated uptake influenced the PET results in three patients (10%) and the SUV_max values in five patients (17%). Of 39 lesions with significant FDG uptake, 15 (38%) were <4 mm thick. Lesion thickness was correlated with lesion SUV_max in FDG-avid lesions only. FDG arterial uptake was not associated with systemic inflammation or NIH criteria. Conclusions PET/CT reveals unique and fundamental features of arterial involvement in TA. PET/CT may be useful in the assessment of local inflammatory and vascular remodelling events independent of systemic inflammation during follow-up, even in lesions in which the arterial wall is <4 mm. The presence of arterial grafts is a potential confounder. Prospective studies are required to correlate PET findings with relevant clinical outcomes.

Assessments of brain glucose metabolism (18 F-FDG-PET) and cerebral amyloid burden (11 C-PiB-PET) in mild cognitive impairment (MCI) have shown highly variable performances when adopted to predict progression to dementia due to Alzheimer’s disease (A