Machine Learning-Based Radiomics Analysis for Identifying KRAS Mutations in Non-Small-Cell Lung Cancer from CT Images: Challenges, Insights and Implications.

Kirsten Rat Sarcoma viral oncogene homolog (KRAS) is a frequently occurring mutation in non-small-cell lung cancer (NSCLC) and influences cancer treatment and disease progression. In this study, a machine learning (ML) pipeline was applied to radiomic features extracted from public and internal CT images to identify KRAS mutations in NSCLC patients. Both datasets were analyzed using parametric ( test) and non-parametric statistical tests (Mann-Whitney U test) and dimensionality reduction techniques.

Standardized Reporting of HCC with LI-RADS and mRECIST: Update on the Situation in Germany.

Online survey about the current status of CT protocols in hepatocellular carcinoma (HCC) in the year 2023/2024. Moreover, the usage of structured reporting using LI-RADS and mRECIST was surveyed and the results were compared with a survey from 2020.Radiologists working in outpatient or inpatient care in Germany were invited.

Real-world federated learning in radiology: hurdles to overcome and benefits to gain.

Objective: Federated Learning (FL) enables collaborative model training while keeping data locally. Currently, most FL studies in radiology are conducted in simulated environments due to numerous hurdles impeding its translation into practice. The few existing real-world FL initiatives rarely communicate specific measures taken to overcome these hurdles.

Standardized diagnosis of gastrointestinal tumors: an update regarding the situation in Germany.

To evaluate the current status of the diagnosis of gastrointestinal tumors in Germany by means of a survey of the oncological imaging working group of the German Radiological Society (DRG) with a focus on the CT protocols being used.Radiologists working in outpatient or inpatient care in Germany were invited. The survey was conducted between 10/2022 and 06/2023 using the SurveyMonkey web tool.

Non-contrast-enhanced MR-angiography of Extracranial Arteries in Acute Ischemic Stroke at 1.5 Tesla Using Relaxation-Enhanced Angiography Without Contrast and Triggering (REACT).

Purpose: To evaluate a novel flow-independent sequence (Relaxation-Enhanced Angiography without Contrast and Triggering (REACT)) for imaging of the extracranial arteries in acute ischemic stroke (AIS) at 1.5 T.

Methods: This retrospective single-center study included 47 AIS patients who received REACT (scan time: 3:01 min) and contrast-enhanced MRA (CE-MRA) of the extracranial arteries at 1.

Real-world response assessment of immune checkpoint inhibition: comparing iRECIST and RECIST 1.1 in melanoma and non-small cell lung cancer patients.

Objectives: To compare immune response evaluation criteria in solid tumors (iRECIST) and response evaluation criteria in solid tumors (RECIST) 1.1 for response assessment of immune checkpoint inhibitor (ICI) therapy in a real-world setting in patients with melanoma and non-small cell lung cancer (NSCLC).

Methods: Two-hundred fifty-two patients with melanoma and NSCLC who received CTLA-4 inhibitor ipilimumab or PD-1 inhibitors nivolumab or pembrolizumab and who underwent staging CT of the chest and abdomen were retrospectively included.

Seeing the primary tumor because of all the trees: Cancer type prediction on low-dimensional data.

The Cancer of Unknown Primary (CUP) syndrome is characterized by identifiable metastases while the primary tumor remains hidden. In recent years, various data-driven approaches have been suggested to predict the location of the primary tumor (LOP) in CUP patients promising improved diagnosis and outcome. These LOP prediction approaches use high-dimensional input data like images or genetic data.

Generative Pre-trained Transformer 4 analysis of cardiovascular magnetic resonance reports in suspected myocarditis: A multicenter study.

Background: Diagnosing myocarditis relies on multimodal data, including cardiovascular magnetic resonance (CMR), clinical symptoms, and blood values. The correct interpretation and integration of CMR findings require radiological expertise and knowledge. We aimed to investigate the performance of Generative Pre-trained Transformer 4 (GPT-4), a large language model, for report-based medical decision-making in the context of cardiac MRI for suspected myocarditis.

ChatGPT yields low accuracy in determining LI-RADS scores based on free-text and structured radiology reports in German language.

Background: To investigate the feasibility of the large language model (LLM) ChatGPT for classifying liver lesions according to the Liver Imaging Reporting and Data System (LI-RADS) based on MRI reports, and to compare classification performance on structured vs. unstructured reports.

Methods: LI-RADS classifiable liver lesions were included from German written structured and unstructured MRI reports with report of size, location, and arterial phase contrast enhancement as minimum inclusion requirements.

Radiomics workflow definition & challenges - German priority program 2177 consensus statement on clinically applied radiomics.

Objectives: Achieving a consensus on a definition for different aspects of radiomics workflows to support their translation into clinical usage. Furthermore, to assess the perspective of experts on important challenges for a successful clinical workflow implementation.

Materials And Methods: The consensus was achieved by a multi-stage process.

Cooperative AI training for cardiothoracic segmentation in computed tomography: An iterative multi-center annotation approach.

Purpose: Radiological reporting is transitioning to quantitative analysis, requiring large-scale multi-center validation of biomarkers. A major prerequisite and bottleneck for this task is the voxelwise annotation of image data, which is time-consuming for large cohorts. In this study, we propose an iterative training workflow to support and facilitate such segmentation tasks, specifically for high-resolution thoracic CT data.

Robustness of radiomic features in healthy abdominal parenchyma of patients with repeated examinations on dual-layer dual-energy CT.

Objectives: Robustness of radiomic features in physiological tissue is an important prerequisite for quantitative analysis of tumor biology and response assessment. In contrast to previous studies which focused on different tumors with mostly short scan-re-scan intervals, this study aimed to evaluate the robustness of radiomic features in cancer-free patients and over a clinically encountered inter-scan interval.

Materials And Methods: Patients without visible tumor burden who underwent at least two portal-venous phase dual energy CT examinations of the abdomen between May 2016 and January 2020 were included, while macroscopic tumor burden was excluded based upon follow-up imaging for all patients (≥3 months).

Potential of GPT-4 for Detecting Errors in Radiology Reports: Implications for Reporting Accuracy.

Background Errors in radiology reports may occur because of resident-to-attending discrepancies, speech recognition inaccuracies, and large workload. Large language models, such as GPT-4 (ChatGPT; OpenAI), may assist in generating reports. Purpose To assess effectiveness of GPT-4 in identifying common errors in radiology reports, focusing on performance, time, and cost-efficiency.

Standardization of a CT Protocol for Imaging Patients with Suspected COVID-19-A RACOON Project.

CT protocols that diagnose COVID-19 vary in regard to the associated radiation exposure and the desired image quality (IQ). This study aims to evaluate CT protocols of hospitals participating in the RACOON (Radiological Cooperative Network) project, consolidating CT protocols to provide recommendations and strategies for future pandemics. In this retrospective study, CT acquisitions of COVID-19 patients scanned between March 2020 and October 2020 (RACOON phase 1) were included, and all non-contrast protocols were evaluated.

The worldwide COVID-19 pandemic caused a decline in sonographic examinations - is this a continuing trend?

Purpose: Due to the increasing number of COVID-19 infections since spring 2020 the patient care workflow underwent changes in Germany. To minimize face-to-face exposure and reduce infection risk, non-time-critical elective medical procedures were postponed. Since ultrasound examinations include non-time-critical elective examinations and often can be substituted by other imaging modalities not requiring direct patient contact, the number of examinations has declined significantly.

Chronic Hyponatremia and Brain Structure and Function Before and After Treatment.

Rationale & Objective: Hyponatremia is the most common electrolyte disorder and is associated with significant morbidity and mortality. This study investigated neurocognitive impairment, brain volume, and alterations in magnetic resonance imaging (MRI)-based measures of cerebral function in patients before and after treatment for hyponatremia.

Study Design: Prospective cohort study.

Compressed SENSE accelerated 3D single-breath-hold late gadolinium enhancement cardiovascular magnetic resonance with isotropic resolution: clinical evaluation.

Aim: The purpose of this study was to investigate the clinical application of Compressed SENSE accelerated single-breath-hold LGE with 3D isotropic resolution compared to conventional LGE imaging acquired in multiple breath-holds.

Material & Methods: This was a retrospective, single-center study including 105 examinations of 101 patients (48.2 ± 16.