Autofluorescence-based tissue characterization enhances clinical prospects of light-sheet-microscopy.

Light sheet fluorescence microscopy (LSFM) is a transformative imaging method that enables the visualization of non-dissected specimen in real-time 3D. Optical clearing of tissues is essential for LSFM, typically employing toxic solvents. Here, we test the applicability of a non-hazardous alternative, ethyl cinnamate (ECi). We comprehensively characterized autofluorescence (AF) spectra in diverse murine tissues-ocular globe, knee, and liver-employing LSFM under various excitation wavelengths (405-785 nm) to test the feasibility of unstained samples for diagnostic purposes, in particular regarding percutaneous biopsies, as they constitute to most harvested type of tissue sample in clinical routine. Ocular globe structures were best discerned with 640 nm excitation. Knee tissue showed complex variation in AF spectra variation influenced by tissue depth and structure. Liver exhibited a unique AF pattern, likely linked to vasculature. Hepatic tissue samples were used to demonstrate the compatibility of our protocol for antibody staining. Furthermore, we employed machine learning to augment raw images and segment liver structures based on AF spectra. Radiologists rated representative samples transferred to the clinical assessment software. Learning-generated images scored highest in quality. Additionally, we investigated an actual murine biopsy. Our study pioneers the application of AF spectra for tissue characterization and diagnostic potential of optically cleared unstained percutaneous biopsies, contributing to the clinical translation of LSFM.

[Computed tomography coronary angiography : What does the nonradiologist expect from the radiologist?] / Computertomographie-Koronarangiographie : Was erwartet der Nicht-Radiologe vom Radiologen?

BACKGROUND: Coronary computed tomography angiography (CCTA) has become a central tool for the primary diagnosis of stable coronary artery disease (CAD). Its integration into the service catalog of the German statutory health insurance will not only transform the way patients are examined and treated but also enhance the collaboration between nonradiologists and radiologists. OBJECTIVE: This article explores the requirements nonradiologists have for CCTA and identifies ways to promote successful interdisciplinary communication. MATERIALS AND METHODS: The study addresses criteria for proper patient selection and preparation for CCTA. It considers the perspectives and needs of patients and various medical specialties, highlighting essential aspects of interdisciplinary communication. RESULTS: CCTA enables precise clarification of CAD and should be used for patients with a pretest probability of chronic CAD between 15 and 50%. Clear action plans in the diagnostic report are crucial to assist general practitioners and cardiologists in treatment planning. Patients expect clear information about the procedure, possible risks, and results. CONCLUSION: Close collaboration between various medical disciplines is essential for the successful implementation of CCTA. Clear, structured diagnostic reports with annotated images, along with regular case discussions and feedback loops, can improve report interpretation and interdisciplinary communication. Patient-friendly reports can make diagnostic results more understandable and enhance patient adherence.

Generative Pre-trained Transformer 4 makes cardiovascular magnetic resonance reports easy to understand.

BACKGROUND: Patients are increasingly using Generative Pre-trained Transformer 4 (GPT-4) to better understand their own radiology findings. PURPOSE: To evaluate the performance of GPT-4 in transforming cardiovascular magnetic resonance (CMR) reports into text that is comprehensible to medical laypersons. METHODS: ChatGPT with GPT-4 architecture was used to generate three different explained versions of 20 various CMR reports (n = 60) using the same prompt: "Explain the radiology report in a language understandable to a medical layperson". Two cardiovascular radiologists evaluated understandability, factual correctness, completeness of relevant findings, and lack of potential harm, while 13 medical laypersons evaluated the understandability of the original and the GPT-4 reports on a Likert scale (1 "strongly disagree", 5 "strongly agree"). Readability was measured using the Automated Readability Index (ARI). Linear mixed-effects models (values given as median [interquartile range]) and intraclass correlation coefficient (ICC) were used for statistical analysis. RESULTS: GPT-4 reports were generated on average in 52 s ± 13. GPT-4 reports achieved a lower ARI score (10 [9-12] vs 5 [4-6]; p < 0.001) and were subjectively easier to understand for laypersons than original reports (1 [1] vs 4 [4,5]; p < 0.001). Eighteen out of 20 (90%) standard CMR reports and 2/60 (3%) GPT-generated reports had an ARI score corresponding to the 8th grade level or higher. Radiologists' ratings of the GPT-4 reports reached high levels for correctness (5 [4, 5]), completeness (5 [5]), and lack of potential harm (5 [5]); with "strong agreement" for factual correctness in 94% (113/120) and completeness of relevant findings in 81% (97/120) of reports. Test-retest agreement for layperson understandability ratings between the three simplified reports generated from the same original report was substantial (ICC: 0.62; p < 0.001). Interrater agreement between radiologists was almost perfect for lack of potential harm (ICC: 0.93, p < 0.001) and moderate to substantial for completeness (ICC: 0.76, p < 0.001) and factual correctness (ICC: 0.55, p < 0.001). CONCLUSION: GPT-4 can reliably transform complex CMR reports into more understandable, layperson-friendly language while largely maintaining factual correctness and completeness, and can thus help convey patient-relevant radiology information in an easy-to-understand manner.

CT-derived Epicardial Adipose Tissue Inflammation Predicts Outcome in Patients Undergoing Transcatheter Aortic Valve Replacement.

PURPOSE: Inflammatory changes in epicardial (EAT) and pericardial adipose tissue (PAT) are associated with increased overall cardiovascular risk. Using routine, preinterventional cardiac CT data, we examined the predictive value of quantity and quality of EAT and PAT for outcome after transcatheter aortic valve replacement (TAVR). MATERIALS AND METHODS: Cardiac CT data of 1197 patients who underwent TAVR at the in-house heart center between 2011 and 2020 were retrospectively analyzed. The amount and density of EAT and PAT were quantified from single-slice CT images at the level of the aortic valve. Using established risk scores and known independent risk factors, a clinical benchmark model (BMI, Chronic kidney disease stage, EuroSCORE 2, STS Prom, year of intervention) for outcome prediction (2-year mortality) after TAVR was established. Subsequently, we tested whether the additional inclusion of area and density values of EAT and PAT in the clinical benchmark model improved prediction. For this purpose, the cohort was divided into a training (n=798) and a test cohort (n=399). RESULTS: Within the 2-year follow-up, 264 patients died. In the training cohort, particularly the addition of EAT density to the clinical benchmark model showed a significant association with outcome (hazard ratio 1.04, 95% CI: 1.01-1.07; P =0.013). In the test cohort, the outcome prediction of the clinical benchmark model was also significantly improved with the inclusion of EAT density (c-statistic: 0.589 vs. 0.628; P =0.026). CONCLUSIONS: EAT density as a surrogate marker of EAT inflammation was associated with 2-year mortality after TAVR and may improve outcome prediction independent of established risk parameters.

Opportunistic CT-derived analysis of fat and muscle tissue composition predicts mortality in patients with cardiogenic shock.

Prognosis estimation in patients with cardiogenic shock (CS) is important to guide clinical decision making. Aim of this study was to investigate the predictive value of opportunistic CT-derived body composition analysis in CS patients. Amount and density of fat and muscle tissue of 152 CS patients were quantified from single-slice CT images at the level of the intervertebral disc space L3/L4. Multivariable Cox regression and Kaplan-Meier survival analyses were performed to evaluate the predictive value of opportunistically CT-derived body composition parameters on the primary endpoint of 30-day mortality. Within the 30-day follow-up, 90/152 (59.2%) patients died. On multivariable analyses, lactate (Hazard Ratio 1.10 [95% Confidence Interval 1.04-1.17]; p = 0.002) and patient age (HR 1.04 [95% CI 1.01-1.07], p = 0.017) as clinical prognosticators, as well as visceral adipose tissue (VAT) area (HR 1.004 [95% CI 1.002-1.007]; p = 0.001) and skeletal muscle (SM) area (HR 0.987 [95% CI 0.975-0.999]; p = 0.043) as imaging biomarkers remained as independent predictors of 30-day mortality. Kaplan-Meier survival analyses showed significantly increased 30-day mortality in patients with higher VAT area (p = 0.015) and lower SM area (p = 0.035). CT-derived VAT and SM area are independent predictors of dismal outcomes in CS patients and have the potential to emerge as new imaging biomarkers available from routine diagnostic CT.

Statin adherence in patients enrolled in the disease management program for coronary artery disease - comparison between patients' and general practitioners' self-reports and patient records.

INTRODUCTION: Patients with coronary artery disease (CAD) should take a statin daily for secondary prevention. However, statin adherence in patients with CAD is low. This study investigated the proportion of adherent patients enrolled in the disease management program for CAD (DMP-CAD). Adherence was examined by comparing patients' self-reports, general practitioners' (GPs) self-reports, and prescription data. METHODS: Between October 2019 and March 2020, all patients enrolled in the DMP-CAD in three GP practices in Germany were invited to participate in the study. Participants completed a questionnaire on the tolerability of statins. Further, prescription data from patient records, low-density lipoprotein (LDL) levels, and GPs' assessment of statin adherence were examined. The Medication Possession Ratio (MPR) served as a measurement tool for adherence. RESULTS: Seventy-four patients were included. MPR showed high statin adherence for most patients (83.8%). However, GPs did not reliably identify non-adherence in their patients. Generally, the mean LDL values were above the guideline recommendations (97.7 ± 27.9 mg/dl), with higher values in the non-adherent (123.6 ± 42 mg/dl) than in the adherent group (93.1 ± 22 mg/dl). Non-adherent patients were more likely to be employed (41.7% vs. 11.3%). DISCUSSION: Patients in this study showed high statin adherence. However, the LDL target value was often not reached. Therefore, GPs should take advantage of the good adherence of their patients and try to lower LDL levels by adjusting the dosage and/or changing the statin prescribed. Future studies should investigate typical characteristics of non-adherent patients in DMP-CAD so that GPs can target these patient groups and improve their adherence.