[Adrenal imaging: anatomy and pathology (literature review)].

This literature review focuses on the normal adrenal gland anatomy and typical imaging features necessary to evaluate benign and malignant lesions. In particular, adenoma, pheochromocytoma, metastases and adrenocortical carcinoma were discussed as some of the most common lesions. For this purpose, a review of relevant local and international literature sources up to January 2021 was conducted.In many cases, adrenal incidentalomas have distinctive features allowing characterization using noninvasive methods. It is possible to suspect a malignant nature and promptly refer the patient for the necessary invasive examinations in some cases. -Computed tomography, especially with intravenous contrast enhancement, is the primary imaging modality because it enables differential diagnosis. Magnetic resonance tomography remains a sensitive method in lesion detection and follow-up but is not very specific for determining the malignant potential. Positron emission computed tomography also remains an additional method and is used mainly for differential diagnosis of malignant tumors, detecting metastases and recurrences after surgical treatment. Ultrasound has a limited role but is nevertheless of great importance in the pediatric population, especially newborns. Promising techniques such as radiomics and dual-energy CT can expand imaging capabilities and improve diagnostic accuracy.Because adrenal lesions are often incidentally detected by imaging performed for other reasons, it is vital to interpret such findings correctly. This review should give the reader a broad overview of how different imaging modalities can evaluate adrenal pathology and guide radiologists and clinicians.

[The future of computer-aided diagnostics in chest computed tomography]. / Perspektivy ispol'zovaniia kompleksnoi komp'iuter-assistirovannoi diagnostiki v otsenke struktur grudnoi kletki.

Recently, more and more attention has been paid to the utility of artificial intelligence in medicine. Radiology differs from other medical specialties with its high digitalization, so most software developers operationalize this area of medicine. The primary condition for machine learning is met because medical diagnostic images have high reproducibility. Today, the most common anatomic area for computed tomography is the thorax, particularly with the widespread lung cancer screening programs using low-dose computed tomography. In this regard, the amount of information that needs to be processed by a radiologist is snowballing. Thus, automatic image analysis will allow more studies to be interpreted. This review is aimed at highlighting the possibilities of machine learning in the chest computed tomography.