Assignment of the Biological Identity of Breast Masses in Contrast-enhanced Spectral Computed Tomography Examinations of Chest Carried Out in Clinical Routine Diagnostics.

Background: Focal breast lesions are observed in up to 5.8% of CT examinations performed in female patients for a wide variety of indications not affecting the breast. To simplify and standardize the further procedure in the case of breast masses visualized by computed tomography (CT), an easy and robust diagnostic approach in assigning surely benign findings, uncertain findings and probably malignant findings is warranted.

Spectral CT imaging for assessment of metastases in melanoma patients: multi-reader evaluation.

Background: Pilot studies have indicated diagnostic benefits from using dual-energy CT (DECT) for staging and follow-up of melanoma patients. The purpose of this study was to investigate the sensitivity, specificity and qualitative assessment of spectral image reconstructions for metastases in melanoma patients in a large-scale, multi-reader evaluation.

Methods: In total, 308 patients with melanoma, 95 patients with metastases and a control group of 213 patients without metastases, who underwent oncologic staging CT of the chest, abdomen and pelvis on a dual-layer dual-energy CT system (dlDECT) were retrospectively included.

Imaging in malignant germ cell tumors involving the hypothalamo-neurohypophyseal axis: the evaluation of the posterior pituitary bright spot is essential.

Purpose: Malignant intracranial germ cell tumors (GCTs) are rare diseases in Western countries. They arise in midline structures and diagnosis is often delayed. We evaluated imaging characteristics and early tumor signs of suprasellar and bifocal GCT on MRI.

Transfer-Learning Deep Radiomics and Hand-Crafted Radiomics for Classifying Lymph Nodes from Contrast-Enhanced Computed Tomography in Lung Cancer.

Objectives: Positron emission tomography (PET) is currently considered the non-invasive reference standard for lymph node (N-)staging in lung cancer. However, not all patients can undergo this diagnostic procedure due to high costs, limited availability, and additional radiation exposure. The purpose of this study was to predict the PET result from traditional contrast-enhanced computed tomography (CT) and to test different feature extraction strategies.

Physiological iodine uptake of the spine's bone marrow in dual-energy computed tomography - using artificial intelligence to define reference values based on 678 CT examinations of 189 individuals.

Purpose: The bone marrow's iodine uptake in dual-energy CT (DECT) is elevated in malignant disease. We aimed to investigate the physiological range of bone marrow iodine uptake after intravenous contrast application, and examine its dependence on vBMD, iodine blood pool, patient age, and sex.

Method: Retrospective analysis of oncological patients without evidence of metastatic disease.

Morphological appearance of the B.1.1.7 mutation of the novel coronavirus 2 (SARS-CoV-2) in chest CT.

Background: Diagnosing a coronavirus disease 2019 (COVID-19) infection with high specificity in chest computed tomography (CT) imaging is considered possible due to distinctive imaging features of COVID-19 pneumonia. Since other viral non-COVID pneumonia show mostly a different distribution pattern, it is reasonable to assume that the patterns observed caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are a consequence of its genetically encoded molecular properties when interacting with the respiratory tissue. As more mutations of the initial SARS-CoV-2 wild-type with varying aggressiveness have been detected in the course of 2021, it became obvious that its genome is in a state of transformation and therefore a potential modification of the specific morphological appearance in CT may occur.

AI support for accurate and fast radiological diagnosis of COVID-19: an international multicenter, multivendor CT study.

Objectives: Differentiation between COVID-19 and community-acquired pneumonia (CAP) in computed tomography (CT) is a task that can be performed by human radiologists and artificial intelligence (AI). The present study aims to (1) develop an AI algorithm for differentiating COVID-19 from CAP and (2) evaluate its performance. (3) Evaluate the benefit of using the AI result as assistance for radiological diagnosis and the impact on relevant parameters such as accuracy of the diagnosis, diagnostic time, and confidence.

Assessment of COVID-19 lung involvement on computed tomography by deep-learning-, threshold-, and human reader-based approaches-an international, multi-center comparative study.

Background: The extent of lung involvement in coronavirus disease 2019 (COVID-19) pneumonia, quantified on computed tomography (CT), is an established biomarker for prognosis and guides clinical decision-making. The clinical standard is semi-quantitative scoring of lung involvement by an experienced reader. We aim to compare the performance of automated deep-learning- and threshold-based methods to the manual semi-quantitative lung scoring.

Dual-Energy CT, Virtual Non-Calcium Bone Marrow Imaging of the Spine: An AI-Assisted, Volumetric Evaluation of a Reference Cohort with 500 CT Scans.

Virtual non-calcium (VNCa) images from dual-energy computed tomography (DECT) have shown high potential to diagnose bone marrow disease of the spine, which is frequently disguised by dense trabecular bone on conventional CT. In this study, we aimed to define reference values for VNCa bone marrow images of the spine in a large-scale cohort of healthy individuals. DECT was performed after resection of a malignant skin tumor without evidence of metastatic disease.