Radiological Imaging of Gastro-Entero-Pancreatic Neuroendocrine Tumors. The Review of Current Literature Emphasizing the Diagnostic Value of Chosen Imaging Methods.

Despite development of radiologic imaging, detection and follow-up of neuroendocrine neoplasms (NENs) still pose a diagnostic challenge, due to the heterogeneity of NEN, their relatively long-term growth, and small size of primary tumor. A set of information obtained by using different radiological imaging tools simplifies a choice of the most appropriate treatment method. Moreover, radiological imaging plays an important role in the assessment of metastatic lesions, especially in the liver, as well as, tumor response to treatment. This article reviews the current, broadly in use imaging modalities which are applied to the diagnosis of GEP-NETs, (the most common type of NENs) and put emphasis on the strengths and limitations of each modality.

Chest Computed Tomography (CT) Severity Scales in COVID-19 Disease: A Validation Study.

BACKGROUND Imaging-based quantitative assessment of lung lesions plays a key role in patient triage and therapeutic decision-making processes. The aim of our study was to validate the Total Severity Score (TSS), Chest Computed Tomography Score (CT-S), and Chest CT Severity Score (CT-SS) scales, which were used to assess the extent of lung inflammation in patients with SARS-CoV-2 infection in terms of interobserver agreement and the correlation of scores with patient clinical condition on the day of the study. MATERIAL AND METHODS A total of 77 chest CT scans collected from 77 consecutive patients hospitalized because of SARS-CoV-2 were included. The scans were assessed independently by 2 radiologists aware of the patients' positive results of RT-PCR tests. Each chest CT was assessed according to the 3 scales. To assess the interobserver agreement of CT scan assessments, Cohen's k and intraclass correlation coefficient (ICC) were calculated. RESULTS For the overall assessment, the k was 0.944 and the ICC was 0.948 for the TSS; the kappa was 0.909 and the ICC was 0.919 for the CT-S; and the k was 0.888 and the ICC was 0.899 for the CT-SS. The CT-SS (r=0.627 for Radiologist 1 and r=0.653 for Radiologist 2) revealed the strongest positive correlation with the patient clinical condition as expressed using the Modified Early Warning Score. CONCLUSIONS The interobserver agreement for the 3 evaluated scales was very good. The CT-SS was found to have the strongest positive relationship with the Modified Early Warning Score.

COVID-19 severity scoring systems in radiological imaging - a review.

The current reference standard to make a definitive diagnosis of SARS-CoV-2 infection is the reverse transcription- polymerase chain reaction assay (rt-PCR). However, radiological imaging plays a crucial role in evaluating the course of COVID-19 and in choosing proper management of infected patients. Chest X-ray (CXR) is generally considered not to be sensitive for the detection of pulmonary abnormalities in the early stage of the disease. However, in the emergency setting CXR can be a useful diagnostic tool for monitoring the rapid progression of lung involvement in COVID-19, especially in patients admitted to intensive care units. The rapid course of SARS-CoV-2 infection and the severity and progression of lung aberrations require a method of radiological evaluation to implement and manage the appropriate treatment for infected patients. Computed tomography (CT) imaging is considered to be the most effective method for the detection of lung abnormalities, especially in the early stage of the disease. Moreover, serial chest CT imaging with different time intervals is also effective in estimating the evolution of the disease from initial diagnosis to discharge from hospital. Despite having low specificity in distinguishing abnormalities in viral infections, the high sensitivity of CT makes this method ideal for assessing the severity of the disease in patients with confirmed COVID-19. In this review, we present and discuss currently available scales that can be used to assess the severity of lung involvement in COVID-19 patients in everyday work, both for CXR and CT imaging.

Ischemic infarct detection, localization, and segmentation in noncontrast CT human brain scans: review of automated methods.

Noncontrast Computed Tomography (NCCT) of the brain has been the first-line diagnosis for emergency evaluation of acute stroke, so a rapid and automated detection, localization, and/or segmentation of ischemic lesions is of great importance. We provide the state-of-the-art review of methods for automated detection, localization, and/or segmentation of ischemic lesions on NCCT in human brain scans along with their comparison, evaluation, and classification. Twenty-two methods are (1) reviewed and evaluated; (2) grouped into image processing and analysis-based methods (11 methods), brain atlas-based methods (two methods), intensity template-based methods (1 method), Stroke Imaging Marker-based methods (two methods), and Artificial Intelligence-based methods (six methods); and (3) properties of these groups of methods are characterized. A new method classification scheme is proposed as a 2 × 2 matrix with local versus global processing and analysis, and density versus spatial sampling. Future studies are necessary to develop more efficient methods directed toward deep learning methods as well as combining the global methods with a high sampling both in space and density for the merged radiologic and neurologic data.