Effect of acquisition techniques, latest kernels, and advanced monoenergetic post-processing for stent visualization with third-generation dual-source CT

PURPOSEThe purpose of this study is to systematically evaluate the effect of tube voltage, current kernels, and monoenergetic post-processing on stent visualization.METHODSA 6 mm chrome-cobalt peripheral stent was placed in a dedicated phantom and scanned with the available tube voltage settings of a third-generation dual-source scanner in single-energy (SE) and dual-energy (DE) mode. Images were reconstructed using the latest convolution kernels and monoenergetic reconstructions (40-190 keV) for DE. The sharpness of stent struts (S), struts width (SW), contrast-to-noise-ratios (CNR), and pseudoenhancement (PE) between the vessel with and without stent were analyzed using an in-house built automatic analysis tool. Measurements were standardized through calculated z-scores. Z-scores were combined for stent (SQ), luminal (LQ), and overall depiction quality (OQ) by adding S and SW, CNR and SW and PE, and S and SW and CNR and PE. Two readers rated overall stent depiction on a 5-point Likert-scale. Agreement was calculated using linear-weighted kappa. Correlations were calculated using Spearman correlation coefficient.RESULTSMaximum values of S and CNR were 169.1 HU/pixel for [DE; 100/ Sn 150 kV; Qr59; 40 keV] and 50.0 for [SE; 70 kV; Bv36]. Minimum values of SW and PE were 2.615 mm for [DE; 80 to 90/ Sn 150 kV; Qr59; 140 to 190 keV] and 0.12 HU for [DE; 80/ Sn 150 kV; Qr36; 190 keV]. Best combined z-scores of SQ, LQ, and OQ were 4.53 for [DE; 100/ Sn 150 kV; Qr 59; 40 keV], 1.23 for [DE; 100/ Sn 150 kV; Qr59; 140 keV] and 2.95 for [DE; 90/ Sn 150 kV; Qr59; 50 keV]. Best OQ of SE was ranked third with 2.89 for [SE; 90 kV; Bv59]. Subjective agreement was excellent (kappa=0.86; P < .001) and correlated well with OQ (rs=0.94, P < .001).CONCLUSIONCombining DE computed tomography (CT) acquisition with the latest kernels and monoenergetic post-processing allows for improved stent visualization as compared with SECT. The best overall results were obtained for monoenergetic reconstructions with 50 keV from DECT 90/Sn 150 kV acquisitions using kernel Qr59

Clinical Text Summarization: Adapting Large Language Models Can Outperform Human Experts

Sifting through vast textual data and summarizing key information imposes a substantial burden on how clinicians allocate their time. Although large language models (LLMs) have shown immense promise in natural language processing (NLP) tasks, their efficacy across diverse clinical summarization tasks has not yet been rigorously examined. In this work, we employ domain adaptation methods on eight LLMs, spanning six datasets and four distinct summarization tasks: radiology reports, patient questions, progress notes, and doctor-patient dialogue. Our thorough quantitative assessment reveals trade-offs between models and adaptation methods in addition to instances where recent advances in LLMs may not lead to improved results. Further, in a clinical reader study with six physicians, we depict that summaries from the best adapted LLM are preferable to human summaries in terms of completeness and correctness. Our ensuing qualitative analysis delineates mutual challenges faced by both LLMs and human experts. Lastly, we correlate traditional quantitative NLP metrics with reader study scores to enhance our understanding of how these metrics align with physician preferences. Our research marks the first evidence of LLMs outperforming human experts in clinical text summarization across multiple tasks. This implies that integrating LLMs into clinical workflows could alleviate documentation burden, empowering clinicians to focus more on personalized patient care and other irreplaceable human aspects of medicine.Comment: 23 pages, 22 figure

Standardised lesion segmentation for imaging biomarker quantitation: a consensus recommendation from ESR and EORTC.

BACKGROUND: Lesion/tissue segmentation on digital medical images enables biomarker extraction, image-guided therapy delivery, treatment response measurement, and training/validation for developing artificial intelligence algorithms and workflows. To ensure data reproducibility, criteria for standardised segmentation are critical but currently unavailable. METHODS: A modified Delphi process initiated by the European Imaging Biomarker Alliance (EIBALL) of the European Society of Radiology (ESR) and the European Organisation for Research and Treatment of Cancer (EORTC) Imaging Group was undertaken. Three multidisciplinary task forces addressed modality and image acquisition, segmentation methodology itself, and standards and logistics. Devised survey questions were fed via a facilitator to expert participants. The 58 respondents to Round 1 were invited to participate in Rounds 2-4. Subsequent rounds were informed by responses of previous rounds. RESULTS/CONCLUSIONS: Items with ≥ 75% consensus are considered a recommendation. These include system performance certification, thresholds for image signal-to-noise, contrast-to-noise and tumour-to-background ratios, spatial resolution, and artefact levels. Direct, iterative, and machine or deep learning reconstruction methods, use of a mixture of CE marked and verified research tools were agreed and use of specified reference standards and validation processes considered essential. Operator training and refreshment were considered mandatory for clinical trials and clinical research. Items with a 60-74% agreement require reporting (site-specific accreditation for clinical research, minimal pixel number within lesion segmented, use of post-reconstruction algorithms, operator training refreshment for clinical practice). Items with ≤ 60% agreement are outside current recommendations for segmentation (frequency of system performance tests, use of only CE-marked tools, board certification of operators, frequency of operator refresher training). Recommendations by anatomical area are also specified

Accelerated apoptotic death and <i>in vivo</i> turnover of erythrocytes in mice lacking functional mitogen- and stress-activated kinase MSK1/2

The mitogen- and stress-activated kinase MSK1/2 plays a decisive role in apoptosis. In analogy to apoptosis of nucleated cells, suicidal erythrocyte death called eryptosis is characterized by cell shrinkage and cell membrane scrambling leading to phosphatidylserine (PS) externalization. Here, we explored whether MSK1/2 participates in the regulation of eryptosis. To this end, erythrocytes were isolated from mice lacking functional MSK1/2 (msk−/−) and corresponding wild-type mice (msk+/+). Blood count, hematocrit, hemoglobin concentration and mean erythrocyte volume were similar in both msk−/− and msk+/+ mice, but reticulocyte count was significantly increased in msk−/− mice. Cell membrane PS exposure was similar in untreated msk−/− and msk+/+ erythrocytes, but was enhanced by pathophysiological cell stressors ex vivo such as hyperosmotic shock or energy depletion to significantly higher levels in msk−/− erythrocytes than in msk+/+ erythrocytes. Cell shrinkage following hyperosmotic shock and energy depletion, as well as hemolysis following decrease of extracellular osmolarity was more pronounced in msk−/− erythrocytes. The in vivo clearance of autologously-infused CFSE-labeled erythrocytes from circulating blood was faster in msk−/− mice. The spleens from msk−/− mice contained a significantly greater number of PS-exposing erythrocytes than spleens from msk+/+ mice. The present observations point to accelerated eryptosis and subsequent clearance of erythrocytes leading to enhanced erythrocyte turnover in MSK1/2-deficient mice

Better together: data harmonization and cross-study analysis of abdominal MRI data from UK Biobank and the German National Cohort

OBJECTIVES: The UK Biobank (UKBB) and German National Cohort (NAKO) are among the largest cohort studies, capturing a wide range of health-related data from the general population, including comprehensive magnetic resonance imaging (MRI) examinations. The purpose of this study was to demonstrate how MRI data from these large-scale studies can be jointly analyzed and to derive comprehensive quantitative image-based phenotypes across the general adult population. MATERIALS AND METHODS: Image-derived features of abdominal organs (volumes of liver, spleen, kidneys, and pancreas; volumes of kidney hilum adipose tissue; and fat fractions of liver and pancreas) were extracted from T1-weighted Dixon MRI data of 17,996 participants of UKBB and NAKO based on quality-controlled deep learning generated organ segmentations. To enable valid cross-study analysis, we first analyzed the data generating process using methods of causal discovery. We subsequently harmonized data from UKBB and NAKO using the ComBat approach for batch effect correction. We finally performed quantile regression on harmonized data across studies providing quantitative models for the variation of image-derived features stratified for sex and dependent on age, height, and weight. RESULTS: Data from 8791 UKBB participants (49.9% female; age, 63 ± 7.5 years) and 9205 NAKO participants (49.1% female, age: 51.8 ± 11.4 years) were analyzed. Analysis of the data generating process revealed direct effects of age, sex, height, weight, and the data source (UKBB vs NAKO) on image-derived features. Correction of data source-related effects resulted in markedly improved alignment of image-derived features between UKBB and NAKO. Cross-study analysis on harmonized data revealed comprehensive quantitative models for the phenotypic variation of abdominal organs across the general adult population. CONCLUSIONS: Cross-study analysis of MRI data from UKBB and NAKO as proposed in this work can be helpful for future joint data analyses across cohorts linking genetic, environmental, and behavioral risk factors to MRI-derived phenotypes and provide reference values for clinical diagnostics

MRI-derived radiomics features of hepatic fat predict metabolic states in individuals without cardiovascular disease.

Rationale and Objectives: To investigate radiomics features of hepatic fat as potential biomarkers of type 2 diabetes mellitus (T2DM) and metabolic syndrome (MetS) in individuals without overt cardiovascular disease, and benchmarking against hepatic proton density fat fraction (PDFF) and the body mass index (BMI). Materials and Methods: This study collected liver radiomics features of 310 individuals that were part of a case-controlled imaging substudy embedded in a prospective cohort. Individuals had known T2DM (n = 39; 12.6 %) and MetS (n = 107; 34.5 %) status, and were divided into stratified training (n = 232; 75 %) and validation (n = 78; 25 %) sets. Six hundred eighty-four MRI radiomics features were extracted for each liver volume of interest (VOI) on T1-weighted dual-echo Dixon relative fat water content (rfwc) maps. Test-retest and inter-rater variance was simulated by additionally extracting radiomics features using noise augmented rfwc maps and deformed volume of interests. One hundred and seventy-one features with test-retest reliability (ICC(1,1)) and inter-rater agreement (ICC(3,k)) of ≥0.85 on the training set were considered stable. To construct predictive random forest (RF) models, stable features were filtered using univariate RF analysis followed by sequential forward aggregation. The predictive performance was evaluated on the independent validation set with area under the curve of the receiver operating characteristic (AUROC) and balanced accuracy (AccuracyB). Results: On the validation set, the radiomics RF models predicted T2DM with AUROC of 0.835 and AccuracyB of 0.822 and MetS with AUROC of 0.838 and AccuracyB of 0.787, outperforming the RF models trained on the benchmark parameters PDFF and BMI. Conclusion: Hepatic radiomics features may serve as potential imaging biomarkers for T2DM and MetS