Image quality assessment of photon-counting CT for patients with prostate cancer receiving radiotherapy.

Background And Purpose: Photon-counting computed tomography (PCCT) offers enhanced image quality, including improvements in contrast, spatial resolution, and noise reduction. In radiotherapy (RT), optimal image quality is critical for accurate tumor and organ-at-risk delineation. However, reconstruction parameter selection often relies on subjective assessment.

Stereotactic Ablative Radiation Therapy of Bone Metastases in an Oligometastatic Setting: One-Year Follow-Up of the BONY-M Phase 2 Trial.

Purpose: SABR is increasingly applied to treat oligometastatic disease (OMD). As patients may be asymptomatic, severe toxicity must be carefully balanced with effect in this setting. The BONY-M trial aimed to determine the efficacy and toxicity after SABR of uncomplicated bone metastases in patients with OMD.

Motion artifacts and image quality in stroke MRI: associated factors and impact on AI and human diagnostic accuracy.

Objectives: To assess the prevalence of motion artifacts and the factors associated with them in a cohort of suspected stroke patients, and to determine their impact on diagnostic accuracy for both AI and radiologists.

Materials And Methods: This retrospective cross-sectional study included brain MRI scans of consecutive adult suspected stroke patients from a non-comprehensive Danish stroke center between January and April 2020. An expert neuroradiologist identified acute ischemic, hemorrhagic, and space-occupying lesions as references.

Should chest computed tomography be routine in patients referred to abdominal computed tomography for major emergency abdominal pathology: an exploratory clinical study.

Purpose: The introduction of optimised care bundles in emergency major abdominal surgery has reduced mortality. Core elements are fast diagnostic work-up with abdominal computed tomography (CT) and surgery without delay. Given the diagnostic challenges in patients with abdominal pain, we aimed to investigate if the addition of a full contrast-enhanced chest CT provides additional clinically significant diagnostic information in patients referred to an emergency abdominal CT.

Impact of spectrum bias on deep learning-based stroke MRI analysis.

Purpose: To evaluate spectrum bias in stroke MRI analysis by excluding cases with uncertain acute ischemic lesions (AIL) and examining patient, imaging, and lesion factors associated with these cases.

Materials And Methods: This single-center retrospective observational study included adults with brain MRIs for suspected stroke between January 2020 and April 2022. Diagnostic uncertain AIL were identified through reader disagreement or low certainty grading by a radiology resident, a neuroradiologist, and the original radiology report consisting of various neuroradiologists.

Centerline-guided reinforcement learning model for pancreatic duct identifications.

Purpose: Pancreatic ductal adenocarcinoma is forecast to become the second most significant cause of cancer mortality as the number of patients with cancer in the main duct of the pancreas grows, and measurement of the pancreatic duct diameter from medical images has been identified as relevant for its early diagnosis.

Approach: We propose an automated pancreatic duct centerline tracing method from computed tomography (CT) images that is based on deep reinforcement learning, which employs an artificial agent to interact with the environment and calculates rewards by combining the distances from the target and the centerline. A deep neural network is implemented to forecast step-wise values for each potential action.

Handheld Ultrasound Devices Used by Newly Certified Operators for Pneumonia in the Emergency Department-A Diagnostic Accuracy Study.

The diagnostic accuracy of handheld ultrasound (HHUS) devices operated by newly certified operators for pneumonia is unknown. This multicenter diagnostic accuracy study included patients prospectively suspected of pneumonia from February 2021 to February 2022 in four emergency departments. The index test was a 14-zone focused lung ultrasound (FLUS) examination, with consolidation with air bronchograms as diagnostic criteria for pneumonia.

DCE-CT parameters as new functional imaging biomarkers at baseline and during immune checkpoint inhibitor therapy in patients with lung cancer - a feasibility study.

Background: With the development of immune checkpoint inhibitors for the treatment of non-small cell lung cancer, the need for new functional imaging techniques and early response assessments has increased to account for new response patterns and the high cost of treatment. The present study was designed to assess the prognostic impact of dynamic contrast-enhanced computed tomography (DCE-CT) on survival outcomes in non-small cell lung cancer patients treated with immune checkpoint inhibitors.

Methods: Thirty-three patients with inoperable non-small-cell lung cancer treated with immune checkpoint inhibitors were prospectively enrolled for DCE-CT as part of their follow-up.

Can clinicians identify community-acquired pneumonia on ultralow-dose CT? A diagnostic accuracy study.

Background: Without increasing radiation exposure, ultralow-dose computed tomography (CT) of the chest provides improved diagnostic accuracy of radiological pneumonia diagnosis compared to a chest radiograph. Yet, radiologist resources to rapidly report the chest CTs are limited. This study aimed to assess the diagnostic accuracy of emergency clinicians' assessments of chest ultralow-dose CTs for community-acquired pneumonia using a radiologist's assessments as reference standard.

Reinforcement learning-based anatomical maps for pancreas subregion and duct segmentation.

Background: The pancreas is a complex abdominal organ with many anatomical variations, and therefore automated pancreas segmentation from medical images is a challenging application.

Purpose: In this paper, we present a framework for segmenting individual pancreatic subregions and the pancreatic duct from three-dimensional (3D) computed tomography (CT) images.

Methods: A multiagent reinforcement learning (RL) network was used to detect landmarks of the head, neck, body, and tail of the pancreas, and landmarks along the pancreatic duct in a selected target CT image.

Risk of hypersensitivity pneumonitis and other interstitial lung diseases following organic dust exposure.

Background: Organic dust is associated with hypersensitivity pneumonitis, and associations with other types of interstitial lung disease (ILD) have been suggested. We examined the association between occupational organic dust exposure and hypersensitivity pneumonitis and other ILDs in a cohort study.

Methods: The study population included all residents of Denmark born in 1956 or later with at least 1 year of gainful employment since 1976.

Consequences of Losing Incidental Pulmonary Nodules to Follow-Up: Unmonitored Nodules Progressing to Stage IV Lung Cancer.

Introduction: Lung cancer is the leading cause of cancer-related death globally. Incidental pulmonary nodules represent a golden opportunity for early diagnosis, which is critical for improving survival rates. This study explores the impact of missed pulmonary nodules on the progression of lung cancer.

The usual Interstitial pneumonia pattern in autoimmune rheumatic diseases.

Usual Interstitial Pneumonia (UIP) is characterized by progression of lung parenchyma that may be observed in various autoimmune rheumatic diseases (ARDs), including rheumatoid arthritis and connective tissue diseases. From a diagnostic point of view, a UIP pattern related to ARDs may display imaging and pathological features able to distinguish it from that related to IPF, such as the "straight-edge" sign at HRCT and lymphoplasmacytic infiltrates at histologic specimens. Multidisciplinary approach (MDD), involving at least pulmonologist, rheumatologist and radiologist, is fundamental in the differential diagnosis process, but MDD is also required in the evaluation of severity, progression and response to treatment, that is based on the combination of changes in symptoms, pulmonary function trends, and, in selected patients, serial CT evaluation.

Commercially Available Chest Radiograph AI Tools for Detecting Airspace Disease, Pneumothorax, and Pleural Effusion.

Background Commercially available artificial intelligence (AI) tools can assist radiologists in interpreting chest radiographs, but their real-life diagnostic accuracy remains unclear. Purpose To evaluate the diagnostic accuracy of four commercially available AI tools for detection of airspace disease, pneumothorax, and pleural effusion on chest radiographs. Materials and Methods This retrospective study included consecutive adult patients who underwent chest radiography at one of four Danish hospitals in January 2020.

Potential impact of texture analysis in contrast enhanced CT in non-small cell lung cancer as a marker of survival: A retrospective feasibility study.

The objective of this feasibility study was to assess computed tomography (CT) texture analysis (CTTA) of pulmonary lesions as a predictor of overall survival in patients with suspected lung cancer on contrast-enhanced computed tomography (CECT). In a retrospective pilot study, 94 patients (52 men and 42 women; mean age, 67.2 ± 10.

Assessment of Correlation between Dual-Energy Ct (De-Ct)-Derived Iodine Concentration and Local Flourodeoxyglucose (Fdg) Uptake in Patients with Primary Non-Small-Cell Lung Cancer.

(1) The current literature contains several studies investigating the correlation between dual-energy-derived iodine concentration (IC) and positron emission tomography (PET)-derived Flourodeoxyglucose (18F-FDG) uptake in patients with non-small-cell lung cancer (NSCLC). In previously published studies, either the entire tumor volume or a region of interest containing the maximum IC or 18F-FDG was assessed. However, the results have been inconsistent.

Early reduction in spectral dual-layer detector CT parameters as favorable imaging biomarkers in patients with metastatic renal cell carcinoma.

Objectives: To associate the early change in DL-CT parameters and HU with survival outcomes and treatment response in patients with metastatic renal cell carcinoma (mRCC).

Methods: DL-CT scans were performed at baseline and after 1 month of checkpoint immunotherapy or tyrosine kinase inhibitor therapy. Scans were reconstructed to conventional CT and DL-CT series, and used for assessment of HU, iodine concentration (IC), and the effective atomic number (Z) in the combined RECISTv.

Aspergilloma complicating previous COVID-19 pneumonitis - a case report.

Aspergillomas are found in pre-existing cavities in pulmonary parenchyma. To the best of our knowledge, aspergilloma has not previously been reported in COVID-19-associated pulmonary architecture distortion combined with barotrauma from invasive mechanical ventilation therapy. We present a case of a 67-year-old woman, who suffered from severe COVID-19 in the summer of 2020 with no suspicion of infection with Aspergillus in the acute phase.

Economic impact of spectral body imaging in diagnosis of patients suspected for occult cancer.

Background: Based on prior studies spectral CT has shown a higher sensitivity for malignant lesions than conventional CT at the cost of lower specificity. For the radiologists, it also offers a higher degree of certainty in the diagnosis of benign lesions. The objective of this study was to evaluate the economic impact of spectral CT in patients suspected of occult cancer in a medical center in Denmark.

Prognostic Utility of Parameters Derived From Pretreatment Dual-Layer Spectral-Detector CT in Patients With Metastatic Renal Cell Carcinoma.

New therapies have emerged for metastatic renal cell carcinoma (mRCC), though corresponding imaging markers are lacking. Dual-layer spectral-detector CT (DLCT) can quantify iodine concentration (IC) and effective atomic number (Z), providing information beyond attenuation that may indicate mRCC prognosis. The purpose of our study was to assess the utility of the DLCT-derived parameters IC and Z for predicting mRCC treatment response and survival.

Impact of Concurrent Use of Artificial Intelligence Tools on Radiologists Reading Time: A Prospective Feasibility Study.

Rational And Objectives: This study investigated how an AI tool impacted radiologists reading time for non-contrast chest CT exams.

Materials And Methods: An AI tool was implemented into the PACS reading workflow of non-contrast chest CT exams between April and May 2020. The reading time was recorded for one CONSULTANT RADIOLOGIST and one RADIOLOGY RESIDENT by an external observer.

Descriptive analysis of long COVID sequelae identified in a multidisciplinary clinic serving hospitalised and non-hospitalised patients.

Background: There are emerging data of long-term effects of coronavirus disease 2019 (COVID-19) comprising a diversity of symptoms. The aim of this study was to systematically describe and measure pulmonary and extra-pulmonary post-COVID-19 complications in relation to acute COVID-19 severity.

Methods: Patients attending a standard of care 3 months post-hospitalisation follow-up visit and those referred by their general practitioner because of persistent post-COVID-19 symptoms were included.