Sex-Specific Prognostic Value of Automated Epicardial Adipose Tissue Quantification on Serial Lung Cancer Screening Chest CT.

Aims: Epicardial adipose tissue (EAT) is a metabolically active fat depot associated with coronary atherosclerosis and cardiovascular (CV) risk. While EAT is a known prognostic marker in lung cancer screening, its sex-specific prognostic value remains unclear. This study investigated sex differences in the prognostic utility of serial EAT measurements on low-dose chest CTs.

First-generation Photon-counting Computed Tomography Angiography Versus Third-generation Dual-energy Computed Tomography Angiography for Peripheral Artery Disease Imaging.

Objective: To investigate the objective performance and subjective image quality of lower extremity CT angiography (CTA) in peripheral artery disease (PAD) through comparison of the first-generation photon-counting CT (PCCT) technology and the third-generation dual source energy-integrating detector CT (DECT) technology.

Materials And Methods: Patients who underwent a CTA either on a PCCT or on a DECT were included in this retrospective analysis. All included patients received a digital subtraction angiography (DSA) as reference standard for stenosis grading.

Obstructive Coronary Artery Disease Improved Prediction by the COME-CCT Pretest Probability Calculator With Cardiac CT.

Background: Combining pretest probability (PTP) with computed tomography angiography (CTA) for diagnosing obstructive coronary artery disease (CAD) has not yet been determined.

Objectives: The purpose of this study was to evaluate the accuracy of PTP calculation alone and with CTA for diagnosing CAD.

Methods: A total of 65 prospective diagnostic accuracy studies of patients clinically referred to invasive coronary angiography with stable chest pain were included in this international collaborative individual patient data Collaborative Meta-Analysis of Cardiac CT (COME-CCT) meta-analysis.

Variability of Metabolic Rate and Distribution Volume Quantification in Whole-Body Parametric PATLAK [F]-FDG PET/CT-A Prospective Trial in Patients with Lung Cancer.

: The recent introduction of whole-body positron emission tomography/ computed tomography (PET/CT) scanners and multi-bed, multi-time point acquisition technique enable calculating fluorodeoxyglucose (FDG) kinetics in the whole body. However, validating parametric, Patlak-derived data is difficult on phantoms. : This prospective study investigated the effect of quantification methods mean, max, and peak on the metabolic rate (MR-FDG) and distribution volume (DV-FDG) quantification, as well as the diagnostic accuracy of parametric Patlak FDG-PET scans in diagnosing lung lesions and lymph node metastases, using histopathology and follow-up as reference standards.

Deep Learning-Enhanced T1-Weighted Imaging for Breast MRI at 1.5T.

: Assessment of a novel deep-learning (DL)-based T1w volumetric interpolated breath-hold (VIBE) sequence in breast MRI in comparison with standard VIBE (VIBE) for image quality evaluation. : Prospective study of 52 breast cancer patients examined at 1.5T breast MRI with T1w VIBE and T1 VIBE sequence.

Histologic Ex Vivo Validation of the [F]SITATE Somatostatin Receptor PET Tracer.

Radiolabeled somatostatin analogs (SSAs), such as [Ga]Ga-DOTA SSAs, have transformed imaging and therapeutic strategies. However, their use is constrained by the high cost of generators and their short half-life. In contrast, [F]SITATE presents a promising alternative, offering the advantage of a longer half-life than Ga, along with the cost-effectiveness of cyclotron-based production.

Evaluating Pre-Interventional Administration of a Liver-Specific Contrast Agent During MRI-Guided Thermal Ablation of Malignant Liver Lesions.

Hepatocyte-specific contrast agent (Gd-EOB-DTPA) can improve the detection of liver lesions during MRI-guided thermal ablation. This study aimed to assess the impact of contrast agent administration on the delineation of the ablation zone during therapy monitoring. From 2010 to 2020, 358 patients with primary and secondary hepatic malignancies underwent MRI-guided thermoablation.

Framework for Environmentally Sustainable Radiology: Call for Collaborative Action and a Health-Centered Focus.

It is imperative that the entire medical imaging sector acts collectively and decisively to reduce its own environmental impact and prepare for the current and future effects of the climate crisis. The Radiology R7 meeting convened in Venice, Italy, on October 10-13, 2024 to discuss environmental sustainability and other key issues facing radiology and the patients served by medical imaging. Radiology R7 delegates agree that collaborative action is urgently needed to transform radiology systems to be climate-resilient, equitable, low-carbon, and sustainable.

SwarmRL: building the future of smart active systems.

This work introduces SwarmRL, a Python package designed to study intelligent active particles. SwarmRL provides an easy-to-use interface for developing models to control microscopic colloids using classical control and deep reinforcement learning approaches. These models may be deployed in simulations or real-world environments under a common framework.

Deep learning-based reconstruction and superresolution for MR-guided thermal ablation of malignant liver lesions.

Objective: This study evaluates the impact of deep learning-enhanced T1-weighted VIBE sequences (DL-VIBE) on image quality and procedural parameters during MR-guided thermoablation of liver malignancies, compared to standard VIBE (SD-VIBE).

Methods: Between September 2021 and February 2023, 34 patients (mean age: 65.4 years; 13 women) underwent MR-guided microwave ablation on a 1.

Deep learning reconstruction for accelerated 3-D magnetic resonance cholangiopancreatography.

Purpose: This study aimed to compare a conventional three-dimensional (3-D) magnetic resonance cholangiopancreatography (MRCP) sequence with a deep learning (DL)-accelerated MRCP sequence (hereafter, MRCP) regarding acquisition time and image quality.

Materials And Methods: We conducted a prospective study of consecutive patients referred for MRCP between November 2023 and April 2024 at a single tertiary center. Each participant underwent 1.

Multi-Center Benchmarking of a Commercially Available Artificial Intelligence Algorithm for Prostate Imaging Reporting and Data System (PI-RADS) Score Assignment and Lesion Detection in Prostate MRI.

Background: The increase in multiparametric magnetic resonance imaging (mpMRI) examinations as a fundamental tool in prostate cancer (PCa) diagnostics raises the need for supportive computer-aided imaging analysis. Therefore, we evaluated the performance of a commercially available AI-based algorithm for prostate cancer detection and classification in a multi-center setting.

Methods: Representative patients with 3T mpMRI between 2017 and 2022 at three different university hospitals were selected.

Determinants of ascending aortic morphology: cross-sectional deep learning-based analysis on 25 073 non-contrast-enhanced NAKO MRI studies.

Aims: Understanding determinants of thoracic aortic morphology is crucial for precise diagnostics and therapeutic approaches. This study aimed to automatically characterize ascending aortic morphology based on 3D non-contrast-enhanced magnetic resonance angiography (NC-MRA) data from the epidemiological cross-sectional German National Cohort (NAKO) and to investigate possible determinants of mid-ascending aortic diameter (mid-AAoD).

Methods And Results: Deep learning (DL) automatically segmented the thoracic aorta and ascending aortic length, volume, and diameter was extracted from 25 073 NC-MRAs.

Impact of the COVID-19 pandemic on cardiac magnetic resonance imaging practices: insights from the MRCT registry.

Purpose: To assess the relationship between the COVID-19 pandemic and the spectrum of indications for cardiac magnetic resonance imaging (MRI) with a focus on myocarditis.

Materials And Methods: This was a retrospective analysis of data from the MRCT registry of the European Society of Cardiovascular Radiology (ESCR). Data regarding indications and diagnoses of myocarditis between January 1, 2018, and April 30, 2024, were extracted.

Guiding AI in radiology: ESR's recommendations for effective implementation of the European AI Act.

This statement has been produced within the European Society of Radiology AI Working Group and identifies the key policies of the EU AI Act as they pertain to medical imaging. It offers specific recommendations to policymakers and the professional community for the effective implementation of the legislation, addressing potential gaps and uncertainties. Key areas include AI literacy, classification rules for high-risk AI systems, data governance, transparency, human oversight, quality management, deployer obligations, regulatory sandboxes, post-market monitoring, information sharing, and market surveillance.

Longitudinal assessment of diffusion-weighted imaging during magnetic resonance-guided radiotherapy in head and neck cancer.

Background: For radiotherapy of head and neck cancer (HNC) magnetic resonance imaging (MRI) plays a pivotal role due to its high soft tissue contrast. Moreover, it offers the potential to acquire functional information through diffusion weighted imaging (DWI) with the potential to personalize treatment. The aim of this study was to acquire repetitive DWI during the course of online adaptive radiotherapy on an 1.

Evaluation of a Deep Learning Denoising Algorithm for Dose Reduction in Whole-Body Photon-Counting CT Imaging: A Cadaveric Study.

Rationale And Objectives: Photon Counting CT (PCCT) offers advanced imaging capabilities with potential for substantial radiation dose reduction; however, achieving this without compromising image quality remains a challenge due to increased noise at lower doses. This study aims to evaluate the effectiveness of a deep learning (DL)-based denoising algorithm in maintaining diagnostic image quality in whole-body PCCT imaging at reduced radiation levels, using real intraindividual cadaveric scans.

Materials And Methods: Twenty-four cadaveric human bodies underwent whole-body CT scans on a PCCT scanner (NAEOTOM Alpha, Siemens Healthineers) at four different dose levels (100%, 50%, 25%, and 10% mAs).

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Clinical validation of a prognostic preclinical magnetic resonance imaging biomarker for radiotherapy outcome in head-and-neck cancer.

Purpose: To retrain a model based on a previously identified prognostic imaging biomarker using apparent diffusion coefficient (ADC) values from diffusion-weighted magnetic resonance imaging (DW-MRI) in a preclinical setting and validate the model using clinical DW-MRI data of patients with locally advanced head-and-neck cancer (HNC) acquired before radiochemotherapy.

Material And Methods: A total of 31 HNC patients underwent T2-weighted and DW-MRI using 3 T MRI before radiochemotherapy (35 x 2 Gy). Gross tumor volumes (GTV) were delineated based on T2-weighted and b500 images.

Standardized Response Assessment in Patients with Advanced Cholangiocarcinoma Treated with Personalized Therapy.

: Current guidelines recommend Cisplatin/Gemcitabine/Durvalumab as first-line treatment for inoperable or recurrent cholangiocarcinoma (CCA). Molecular tumor boards (MTB) have the expertise to support organ-specific tumor boards with evidence-based treatment recommendations for subsequent lines of treatment, based on genomic tumor data and scientific evidence. This study evaluates the adoption of an MTB at a comprehensive cancer center in Germany and whether actionable genetic alterations are associated with specific imaging phenotypes.

Online Radiotherapy: The Paradigm Shift to Real-Time Adaptive Radiotherapy.

Background: Adaptive radiotherapy (ART) involves the continuous adaptation of the radiation plan according to patient- and tumorspecific feedback. In online ART, the plan is optimized in real time during the treatment; in offline ART, the plan is recalculated between treatment sessions. Hybrid linear accelerators with integrated CT, MRI, or PET are required to perform online ART.

Reliability of Automated RECIST 1.1 and Volumetric RECIST Target Lesion Response Evaluation in Follow-Up CT-A Multi-Center, Multi-Observer Reading Study.

Objectives: To evaluate the performance of a custom-made convolutional neural network (CNN) algorithm for fully automated lesion tracking and segmentation, as well as RECIST 1.1 evaluation, in longitudinal computed tomography (CT) studies compared to a manual Response Evaluation Criteria in Solid Tumors (RECIST 1.1) evaluation performed by three radiologists.

Feasibility, classification and potential clinical impact of non-invasive delineation of abdominal lymphatic vessels in patients following TCPC with T2 weighted MRI.

Recent research in patients with functionally univentricular hearts (UVH) is focusing on pathologies of the lymphatic vessels. Morphology of the abdominal lymphatic vessels was analyzed by MRI in patients with UVH following total cavopulmonary connection (TCPC) and it was examined, if clinical and laboratory parameters correlate with changes after TCPC. We prospectively examined 33 patients at the age of 19.

Perioperative Observations and Outcome in Surgical Treatment of Malignant Peripheral Nerve Sheath Tumors.

Background/objectives: This retrospective observational study aimed to investigate the perioperative outcome in Malignant Peripheral Nerve Sheath Tumors (MPNSTs) with and without relation to Neurofibromatosis Type 1 (NF1) and to detect possible influencing factors.

Methods: Clinical reports, histopathological evaluations, imaging, and treatment characteristics were reviewed in 35 operated MPNSTs in 33 patients. Possible predictive valuables included disease type, preoperative tumor volume, SUV and MIB-1 proliferation index, resection margins, the presence of metastasis, and whether radio-/chemotherapy was received.