Marcus R. Makowski’s research while affiliated with Charité Universitätsmedizin Berlin and other places

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. It employs an advanced imaging sequence and reconstruction allowing isotropic 3D UTE MRI in clinically acceptable scan times. Methods In our prospective study, 42 patients with 136 lymph nodes had undergone venous computed tomography and pulmonary MRI scans with UTE within a 14-day interval. 3D isotropic UTE images were acquired using FLORET (fermat looped, orthogonally encoded trajectories). Background-corrected phase images (UTE-P) and magnitude images were reconstructed simultaneously from the UTE-Signal. Three radiologists performed a blinded reading in which all lymph nodes with a short-axis diameter (SAD) of at least 0.5 cm were detected. Detection rates and performance metrics of UTE-P for all lymph node regions and for pathologic (SAD ≥10 mm) and nonpathologic lymph nodes (SAD <10 mm) were calculated using computed tomography as a reference. The interreader agreement defined as the presence or absence of lymph nodes based on patient and region was calculated using Fleiss kappa (κ). Findings In the phase images, pathologic lymph nodes in the mediastinal and hilar region were detected with a high diagnostic confidence due to the achieved water-fat contrast (average sensitivity, specificity, positive predictive value, and negative predictive value of 95.83% [confidence interval (CI), 92.76%–98.91%], 100%, 100%, and 99.32% [CI, 98.08%–100%]). Stepwise inclusion of all lymph node regions and nonpathologic lymph nodes was associated with a moderate decrease resulting in an average sensitivity, specificity, positive predictive value, and negative predictive value of 77.9% (CI, 70.9%–84.7%), 99.4% (CI, 98.7%–99.9%), 97.0% (CI, 93.4%–99.7%), and 94.7% (CI, 92.8%–96.4%) for the inclusion of all lymph nodes sizes and regions. Interreader agreement was almost perfect (κ = 0.92). Conclusions Pathological lymph nodes in the mediastinal and hilar region can be detected in phase-images with high diagnostic confidence, thanks to the ability of the phase images to depict water-fat contrast in combination with high spatial 3D resolution, extending the clinical applicability of UTE into the simultaneous assessment of lung parenchyma and lymph nodes.

All tissues in the human body are composed of cells that are embedded in the extracellular matrix. The extracellular matrix has, besides its structural role, several important functions. These functions include important regulatory mechanisms for signal transduction and matrix cell interactions. If pathological processes, e.g., in atherosclerosis or aortic aneurysms occur, the extracellular matrix changes in response. This includes alterations in the structural and functional components of the extracellular matrix. While traditional imaging technologies, such as X-ray or computed tomography (CT), are mainly aimed at imaging morphological changes, molecular magnetic resonance (MR) imaging is a technique that enables the visualization and quantification of pathological changes on a molecular scale. Different techniques can be used for molecular MR imaging. The most commonly employed techniques include the use of specific molecular magnetic resonance probes. These probes are, in most cases, either based on iron oxide particles or gadolinium chelates for signal generation. Aortic abdominal aneurysms represent an irreversible dilation of the aortic wall which could cause severe consequences, including wall rupture with a mortality rate >90%. Due to the absence of symptoms during the development of aortic aneurysms, early diagnosis remains challenging. In the following chapter, we will outline major developments regarding extracellular matrix–specific molecular magnetic resonance imaging for the assessment of aortic aneurysms.

Background Dark-field radiography imaging exploits the wave character of x-rays to measure small-angle scattering on material interfaces, providing structural information with low radiation exposure. We explored the potential of dark-field imaging of bone microstructure to improve the assessment of bone strength in osteoporosis. Methods We prospectively examined 14 osteoporotic/osteopenic and 21 non-osteoporotic/osteopenic human cadaveric vertebrae (L2–L4) with a clinical dark-field radiography system, micro-computed tomography (CT), and spectral CT. Dark-field images were obtained in both vertical and horizontal sample positions. Bone microstructural parameters (trabecular number, Tb.N; trabecular thickness, Tb.Th; bone volume fraction, BV/TV; degree of anisotropy, DA) were measured using standard ex vivo micro-CT, while hydroxyapatite density was measured using spectral CT. Correlations were assessed using Spearman rank correlation coefficients. Results The measured dark-field signal was lower in osteoporotic/osteopenic vertebrae (vertical position, 0.23 ± 0.05 versus 0.29 ± 0.04, p < 0.001; horizontal position, 0.28 ± 0.06 versus 0.34 ± 0.04, p = 0.003). The dark-field signal from the vertical position correlated significantly with Tb.N ( ρ = 0.46, p = 0.005), BV/TV ( ρ = 0.45, p = 0.007), DA ( ρ = -0.43, p = 0.010), and hydroxyapatite density ( ρ = 0.53, p = 0.010). The calculated ratio of vertical/horizontal dark-field signal correlated significantly with Tb.N ( ρ = 0.43, p = 0.011), BV/TV ( ρ = 0.36, p = 0.032), DA ( ρ = -0.51, p = 0.002), and hydroxyapatite density ( ρ = 0.42, p = 0.049). Conclusion Dark-field radiography is a feasible modality for drawing conclusions on bone microarchitecture in human cadaveric vertebral bone. Relevance statement Gaining knowledge of the microarchitecture of bone contributes crucially to predicting bone strength in osteoporosis. This novel radiographic approach based on dark-field x-rays provides insights into bone microstructure at a lower radiation exposure than that of CT modalities. Key Points Dark-field radiography can give information on bone microstructure with low radiation exposure. The dark-field signal correlated positively with bone microstructure parameters. Dark-field signal correlated negatively with the degree of anisotropy. Dark-field radiography helps to determine the directionality of trabecular loss. Graphical Abstract

Structured reporting (SR) has long been a goal in radiology to standardize and improve the quality of radiology reports. Despite evidence that SR reduces errors, enhances comprehensiveness, and increases adherence to guidelines, its widespread adoption has been limited. Recently, large language models (LLMs) have emerged as a promising solution to automate and facilitate SR. Therefore, this narrative review aims to provide an overview of LLMs for SR in radiology and beyond. We found that the current literature on LLMs for SR is limited, comprising ten studies on the generative pre-trained transformer (GPT)-3.5 ( n = 5) and/or GPT-4 ( n = 8), while two studies additionally examined the performance of Perplexity and Bing Chat or IT5. All studies reported promising results and acknowledged the potential of LLMs for SR, with six out of ten studies demonstrating the feasibility of multilingual applications. Building upon these findings, we discuss limitations, regulatory challenges, and further applications of LLMs in radiology report processing, encompassing four main areas: documentation, translation and summarization, clinical evaluation, and data mining. In conclusion, this review underscores the transformative potential of LLMs to improve efficiency and accuracy in SR and radiology report processing. Key Points Question How can LLMs help make SR in radiology more ubiquitous ? Findings Current literature leveraging LLMs for SR is sparse but shows promising results, including the feasibility of multilingual applications . Clinical relevance LLMs have the potential to transform radiology report processing and enable the widespread adoption of SR. However, their future role in clinical practice depends on overcoming current limitations and regulatory challenges, including opaque algorithms and training data .

Purpose: To quantitatively and qualitatively evaluate and compare the performance of leading large language models (LLMs), including proprietary models (GPT-4, GPT-3.5 Turbo, Claude-3-Opus, and Gemini Ultra) and open-source models (Mistral-7b and Mistral-8×7b), in simplifying 109 interventional radiology reports. Methods: Qualitative performance was assessed using a five-point Likert scale for accuracy, completeness, clarity, clinical relevance, naturalness, and error rates, including trust-breaking and post-therapy misconduct errors. Quantitative readability was assessed using Flesch Reading Ease (FRE), Flesch-Kincaid Grade Level (FKGL), SMOG Index, and Dale-Chall Readability Score (DCRS). Paired t-tests and Bonferroni-corrected p-values were used for statistical analysis. Results: Qualitative evaluation showed no significant differences between GPT-4 and Claude-3-Opus for any metrics evaluated (all Bonferroni-corrected p-values: p = 1), while they outperformed other assessed models across five qualitative metrics (p < 0.001). GPT-4 had the fewest content and trust-breaking errors, with Claude-3-Opus second. However, all models exhibited some level of trust-breaking and post-therapy misconduct errors, with GPT-4-Turbo and GPT-3.5-Turbo with few-shot prompting showing the lowest error rates, and Mistral-7B and Mistral-8×7B showing the highest. Quantitatively, GPT-4 surpassed Claude-3-Opus in all readability metrics (all p < 0.001), with a median FRE score of 69.01 (IQR: 64.88-73.14) versus 59.74 (IQR: 55.47-64.01) for Claude-3-Opus. GPT-4 also outperformed GPT-3.5-Turbo and Gemini Ultra (both p < 0.001). Inter-rater reliability was strong (κ = 0.77-0.84). Conclusions: GPT-4 and Claude-3-Opus demonstrated superior performance in generating simplified IR reports, but the presence of errors across all models, including trust-breaking errors, highlights the need for further refinement and validation before clinical implementation. Clinical relevance/applications: With the increasing complexity of interventional radiology (IR) procedures and the growing availability of electronic health records, simplifying IR reports is critical to improving patient understanding and clinical decision-making. This study provides insights into the performance of various LLMs in rewriting IR reports, which can help in selecting the most suitable model for clinical patient-centered applications.

Background The successful integration of artificial intelligence (AI) in healthcare depends on the global perspectives of all stakeholders. This study aims to answer the research question: What are the attitudes of medical, dental, and veterinary students towards AI in education and practice, and what are the regional differences in these perceptions? Methods An anonymous online survey was developed based on a literature review and expert panel discussions. The survey assessed students' AI knowledge, attitudes towards AI in healthcare, current state of AI education, and preferences for AI teaching. It consisted of 16 multiple-choice items, eight demographic queries, and one free-field comment section. Medical, dental, and veterinary students from various countries were invited to participate via faculty newsletters and courses. The survey measured technological literacy, AI knowledge, current state of AI education, preferences for AI teaching, and attitudes towards AI in healthcare using Likert scales. Data were analyzed using descriptive statistics, Mann–Whitney U-test, Kruskal–Wallis test, and Dunn-Bonferroni post hoc test. Results The survey included 4313 medical, 205 dentistry, and 78 veterinary students from 192 faculties and 48 countries. Most participants were from Europe (51.1%), followed by North/South America (23.3%) and Asia (21.3%). Students reported positive attitudes towards AI in healthcare (median: 4, IQR: 3–4) and a desire for more AI teaching (median: 4, IQR: 4–5). However, they had limited AI knowledge (median: 2, IQR: 2–2), lack of AI courses (76.3%), and felt unprepared to use AI in their careers (median: 2, IQR: 1–3). Subgroup analyses revealed significant differences between the Global North and South ( r = 0.025 to 0.185, all P < .001) and across continents ( r = 0.301 to 0.531, all P < .001), with generally small effect sizes. Conclusions This large-scale international survey highlights medical, dental, and veterinary students' positive perceptions of AI in healthcare, their strong desire for AI education, and the current lack of AI teaching in medical curricula worldwide. The study identifies a need for integrating AI education into medical curricula, considering regional differences in perceptions and educational needs. Trial registration Not applicable (no clinical trial).

Atherosclerosis is a chronic inflammatory condition of the arteries and represents the primary cause of various cardiovascular diseases. Despite ongoing progress, finding effective anti-inflammatory therapeutic strategies for atherosclerosis remains a challenge. Here, we assessed the potential of molecular magnetic resonance imaging (MRI) to visualize the effects of 01BSUR, an anti-interleukin-1β monoclonal antibody, for treating atherosclerosis in a murine model. Male apolipoprotein E-deficient mice were divided into a therapy group (01BSUR, 2 × 0.3 mg/kg subcutaneously, n = 10) and control group (no treatment, n = 10) and received a high-fat diet for eight weeks. The plaque burden was assessed using an elastin-targeted gadolinium-based contrast probe (0.2 mmol/kg intravenously) on a 3 T MRI scanner. T1-weighted imaging showed a significantly lower contrast-to-noise (CNR) ratio in the 01BSUR group (pre: 3.93042664; post: 8.4007067) compared to the control group (pre: 3.70679168; post: 13.2982156) following administration of the elastin-specific MRI probe (p < 0.05). Histological examinations demonstrated a significant reduction in plaque size (p < 0.05) and a significant decrease in plaque elastin content (p < 0.05) in the treatment group compared to control animals. This study demonstrated that 01BSUR hinders the progression of atherosclerosis in a mouse model. Using an elastin-targeted MRI probe, we could quantify these therapeutic effects in MRI.

The successful implementation of artificial intelligence (AI) in healthcare is dependent upon the acceptance of this technology by key stakeholders, particularly patients, who are the primary beneficiaries of AI-driven outcomes. This international, multicenter, cross-sectional study assessed the attitudes of hospital patients towards AI in healthcare across 43 countries. A total of 13806 patients at 74 hospitals were surveyed between February and November 2023, with 64.8% from the Global North and 35.2% from the Global South. The findings indicate a predominantly favorable general view of AI in healthcare, with 57.6% of respondents expressing a positive attitude. However, attitudes exhibited notable variation based on demographic characteristics, health status, and technological literacy. Female respondents and those with poorer health status exhibited fewer positive attitudes towards AI use in medicine. Conversely, higher levels of AI knowledge and frequent use of technology devices were associated with more positive attitudes. It is noteworthy that less than half of the participants expressed positive attitudes regarding all items pertaining to trust in AI. The lowest level of trust was observed for the accuracy of AI in providing information regarding treatment responses. Patients exhibited a strong preference for explainable AI and physician-led decision-making, even if it meant slightly compromised accuracy. This large-scale, multinational study provides a comprehensive perspective on patient attitudes towards AI in healthcare across six continents. Findings suggest a need for tailored AI implementation strategies that consider patient demographics, health status, and preferences for explainable AI and physician oversight. All study data has been made publicly available to encourage replication and further investigation.