Evaluating large language model-generated brain MRI protocols: performance of GPT4o, o3-mini, DeepSeek-R1 and Qwen2.5-72B.

Objectives: To evaluate the potential of LLMs to generate sequence-level brain MRI protocols.

Materials And Methods: This retrospective study employed a dataset of 150 brain MRI cases derived from local imaging request forms. Reference protocols were established by two neuroradiologists.

Segmenting Whole-Body MRI and CT for Multiorgan Anatomic Structure Delineation.

Purpose To develop and validate MRSegmentator, a retrospective cross-modality deep learning model for multiorgan segmentation of MRI scans. Materials and Methods This retrospective study trained MRSegmentator on 1,200 manually annotated UK Biobank Dixon MRI sequences (50 participants), 221 in-house abdominal MRI sequences (177 patients), and 1228 CT scans from the TotalSegmentator-CT dataset. A human-in-the-loop annotation workflow leveraged cross-modality transfer learning from an existing CT segmentation model to segment 40 anatomic structures.

AI-Enhanced 3D Gradient Echo MRI: A Radiation-Free Alternative to CT for Lung Nodule Detection and Lung-RADS Classification.

Rationale And Objectives: This study aims to evaluate the capabilities of a 3D gradient echo MRI sequence for the detection and classification of pulmonary nodules, specifically in relation to the lung CT screening reporting and data system (Lung-RADS).

Materials And Methods: In a prospective trial, 75 patients (mean age 65±12years; 44% women) with benign and malignant lung nodules (March 2022-July 2024) underwent chest CT and 3D gradient echo MRI using parallel imaging, compressed sensing, and AI acceleration (CSAI). Three radiologists (experience: 4, 9, and 10 years) assessed detection rates, nodule size, morphology, and Lung-RADS classification in a blinded study.

Privacy-Preserving Generation of Structured Lymphoma Progression Reports from Cross-sectional Imaging: A Comparative Analysis of Llama 3.3 and Llama 4.

Efficient processing of radiology reports for monitoring disease progression is crucial in oncology. Although large language models (LLMs) show promise in extracting structured information from medical reports, privacy concerns limit their clinical implementation. This study evaluates the feasibility and accuracy of two of the most recent Llama models for generating structured lymphoma progression reports from cross-sectional imaging data in a privacy-preserving, real-world clinical setting.

Raising Awareness: Real World Data On Palliative Care for Advanced Pediatric Cancers in Bavaria.

Introduction: Early integration of palliative care in children with cancer provides a variety of positive effects and is recommended at diagnosis. However, barriers often delay its implementation, and palliative care remains underutilized. This study provides real-world data on palliative care and integration in pediatric oncology in a high-income country.

Data extraction from free-text stroke CT reports using GPT-4o and Llama-3.3-70B: the impact of annotation guidelines.

Background: To evaluate the impact of an annotation guideline on the performance of large language models (LLMs) in extracting data from stroke computed tomography (CT) reports.

Methods: The performance of GPT-4o and Llama-3.3-70B in extracting ten imaging findings from stroke CT reports was assessed in two datasets from a single academic stroke center.

Intermuscular adipose tissue and lean muscle mass assessed with MRI in people with chronic back pain in Germany: a retrospective observational study.

Background: Chronic back pain (CBP) affects over 80 million people in Europe, contributing to substantial healthcare costs and disability. Understanding modifiable risk factors, such as muscle composition, may aid in prevention and treatment. This study investigates the association between lean muscle mass (LMM) and intermuscular adipose tissue (InterMAT) with CBP using noninvasive whole-body magnetic resonance imaging (MRI).

Complications of image-guided liver biopsies: Results of a nationwide database analysis.

Background And Aims: Liver biopsy is the gold standard for evaluating liver diseases, the diagnosis of liver fibrosis or liver cirrhosis and malignancy. However, it is susceptible to complications, and safety data on liver biopsies remain scarce. The following study examined the complication rates following percutaneous liver biopsies.

Lung volume assessment for mean dark-field coefficient calculation using different determination methods.

Background: Accurate lung volume determination is crucial for reliable dark-field imaging. We compared different approaches for the determination of lung volume in mean dark-field coefficient calculation.

Methods: In this retrospective analysis of data prospectively acquired between October 2018 and October 2020, patients at least 18 years of age who underwent chest computed tomography (CT) were screened for study participation.

Evaluation of a Retrieval-Augmented Generation-Powered Chatbot for Pre-CT Informed Consent: a Prospective Comparative Study.

This study aims to investigate the feasibility, usability, and effectiveness of a Retrieval-Augmented Generation (RAG)-powered Patient Information Assistant (PIA) chatbot for pre-CT information counseling compared to the standard physician consultation and informed consent process. This prospective comparative study included 86 patients scheduled for CT imaging between November and December 2024. Patients were randomly assigned to either the PIA group (n = 43), who received pre-CT information via the PIA chat app, or the control group (n = 43), with standard doctor-led consultation.

CT-Derived Quantitative Image Features Predict Neoadjuvant Treatment Response in Adenocarcinoma of the Gastroesophageal Junction with High Accuracy.

Background: The purpose of this retrospective study was to evaluate the value of contrast-enhanced computed tomography (CE-CT) image features at baseline and after neoadjuvant chemotherapy in predicting histopathological response in patients with adenocarcinoma of the gastroesophageal junction (GEJ).

Methods: A total of 105 patients with a diagnosis of adenocarcinoma of the GEJ were examined by CE-CT at baseline and preoperatively after neoadjuvant chemotherapy. All patients underwent surgical resection.

Leveraging Phase Information of 3D Isotropic Ultrashort Echo Time Pulmonary MRI for the Detection of Thoracic Lymphadenopathy: Toward an All-in-One Scan Solution.

Background: Ultrashort echo time (UTE) allows imaging of tissues with short relaxation times, but it comes with the expense of long scan times. Magnitude images of UTE magnetic resonance imaging (MRI) are widely used in pulmonary imaging due to excellent parenchymal signal, but have insufficient contrast for other anatomical regions of the thorax. Our work investigates the value of UTE phase images (UTE-Ps)-generated simultaneously from the acquired UTE signal used for the magnitude images-for the detection of thoracic lymph nodes based on water-fat contrast.

Multilingual feasibility of GPT-4o for automated Voice-to-Text CT and MRI report transcription.

Purpose: Large language models (LLMs) promise to streamline radiology reporting. With the release of OpenAI's GPT-4o (Generative Pre-trained Transformers-4 omni), which processes not only text but also speech, multimodal LLMs might now also be used as medical speech recognition software for radiology reporting in multiple languages. This proof-of-concept study investigates the feasibility of using GPT-4o for automated voice-to-text transcription of radiology reports in English and German.

Speed and efficiency: evaluating pulmonary nodule detection with AI-enhanced 3D gradient echo imaging.

Objectives: Evaluating the diagnostic feasibility of accelerated pulmonary MR imaging for detection and characterisation of pulmonary nodules with artificial intelligence-aided compressed sensing.

Materials And Methods: In this prospective trial, patients with benign and malignant lung nodules admitted between December 2021 and December 2022 underwent chest CT and pulmonary MRI. Pulmonary MRI used a respiratory-gated 3D gradient echo sequence, accelerated with a combination of parallel imaging, compressed sensing, and deep learning image reconstruction with three different acceleration factors (CS-AI-7, CS-AI-10, and CS-AI-15).

Comparison of Virtual Non-Contrast and True Non-Contrast CT Images Obtained by Dual-Layer Spectral CT in COPD Patients.

Chronic obstructive pulmonary disease (COPD) is one of the leading causes of death. Recent studies have underlined the importance of non-contrast-enhanced chest CT scans not only for emphysema progression quantification, but for correlation with clinical outcomes as well. As about 40 percent of the 300 million CT scans per year are contrast-enhanced, no proper emphysema quantification is available in a one-stop-shop approach for patients with known or newly diagnosed COPD.

Spectral computed tomography angiography using a gadolinium-based contrast agent for imaging of pathologies of the aorta.

Objectives: Especially patients with aortic aneurysms and multiple computed tomography angiographies (CTA) might show medical conditions which oppose the use of iodine-based contrast agents. CTA using monoenergetic reconstructions from dual layer CT and gadolinium (Gd-)based contrast agents might be a feasible alternative in these patients. Therefore, the purpose of this study was to evaluate the feasibility of clinical spectral CTA with a Gd-based contrast agent in patients with aortic aneurysms.

Development of an image-based Random Forest classifier for prediction of surgery duration of laparoscopic sigmoid resections.

Purpose: Sigmoid diverticulitis is a disease with a high socioeconomic burden, accounting for a high number of left-sided colonic resections worldwide. Modern surgical scheduling relies on accurate prediction of operation times to enhance patient care and optimize healthcare resources. This study aims to develop a predictive model for surgery duration in laparoscopic sigmoid resections, based on preoperative CT biometric and demographic patient data.

Evaluating Treatment Response in GEJ Adenocarcinoma: The Role of Pretherapeutic and Posttherapeutic Iodine Mapping.

Background: Neoadjuvant therapy regimens have significantly improved the prognosis of GEJ (gastroesophageal junction) cancer; however, there are a significant percentage of patients who benefit from earlier resection or adapted therapy regimens, and the true response rate can only be determined histopathologically. Methods that allow preoperative assessment of response are lacking.

Purpose: The purpose of this retrospective study is to assess the potential of pretherapeutic and posttherapeutic spectral CT iodine density (IoD) in predicting histopathological response to neoadjuvant chemotherapy in patients diagnosed with adenocarcinoma of the GEJ.

Machine learning assisted feature identification and prediction of hemodynamic endpoints using computed tomography in patients with CTEPH.

Chronic thromboembolic pulmonary hypertension (CTEPH) is a rare but potentially curable cause of pulmonary hypertension (PH). Currently PH is diagnosed by right heart catheterisation. Computed tomography (CT) is used for ruling out other causes and operative planning.

Evaluation of GPT-4's Chest X-Ray Impression Generation: A Reader Study on Performance and Perception.

Exploring the generative capabilities of the multimodal GPT-4, our study uncovered significant differences between radiological assessments and automatic evaluation metrics for chest x-ray impression generation and revealed radiological bias.

CT Attenuation of Hepatic Pancreatic Cancer Metastases Correlates with Prognostically Detrimental Metastatic Necrosis.

Percutaneous CT-guided biopsy is a frequently performed procedure for the confirmation and molecular workup of hepatic metastases of pancreatic ductal adenocarcinoma (PDAC). Tumor necrosis of primary PDAC has shown a negative prognostic impact in recent studies. This study aims to examine predictability in CT scans and the prognostic impact of necrosis in hepatic metastases of PDAC.

Development and Validation of a Deep Learning Algorithm to Differentiate Colon Carcinoma From Acute Diverticulitis in Computed Tomography Images.

Importance: Differentiating between malignant and benign etiology in large-bowel wall thickening on computed tomography (CT) images can be a challenging task. Artificial intelligence (AI) support systems can improve the diagnostic accuracy of radiologists, as shown for a variety of imaging tasks. Improvements in diagnostic performance, in particular the reduction of false-negative findings, may be useful in patient care.

Prospectively Accelerated T2-Weighted Imaging of the Prostate by Combining Compressed SENSE and Deep Learning in Patients with Histologically Proven Prostate Cancer.

Background: To assess the performance of prospectively accelerated and deep learning (DL) reconstructed T2-weighted (T2w) imaging in volunteers and patients with histologically proven prostate cancer (PCa).

Methods: Prospectively undersampled T2w datasets were acquired with acceleration factors of 1.7 (reference), 3.

Algorithmic transparency and interpretability measures improve radiologists' performance in BI-RADS 4 classification.

Objective: To evaluate the perception of different types of AI-based assistance and the interaction of radiologists with the algorithm's predictions and certainty measures.

Methods: In this retrospective observer study, four radiologists were asked to classify Breast Imaging-Reporting and Data System 4 (BI-RADS4) lesions (n = 101 benign, n = 99 malignant). The effect of different types of AI-based assistance (occlusion-based interpretability map, classification, and certainty) on the radiologists' performance (sensitivity, specificity, questionnaire) were measured.

Cost-Effectiveness of Artificial Intelligence Support in Computed Tomography-Based Lung Cancer Screening.

Background: Lung cancer screening is already implemented in the USA and strongly recommended by European Radiological and Thoracic societies as well. Upon implementation, the total number of thoracic computed tomographies (CT) is likely to rise significantly. As shown in previous studies, modern artificial intelligence-based algorithms are on-par or even exceed radiologist's performance in lung nodule detection and classification.