Baseline MRI Examination in the NAKO Health Study­Findings on Feasibility, Participation and Dropout Rates, Comfort, and Image Quality.

BACKGROUND: Magnetic resonance imaging (MRI) yields important information on the development and current status of many different diseases. Whole-body MRI was accordingly made a part of the multicenter, population-based NAKO Health Study. The present analysis concerns the feasibility of the baseline MRI examination and various aspects of quality assurance over the period 2014-2019. METHODS: 32 252 participants in the NAKO Health Study, aged 20 to 74, who had no contraindication to MRI were invited to undergo scanning in one of five MRI study centers across Germany. The whole-body MRI scan took about one hour and consisted of sequences for the visualization of structural and functional features of the brain, musculoskeletal system, cardiovascular system, and thoracoabdominal system. A comprehensive quality-assurance assessment was carried out, with evaluation of adverse events, the completeness of the MRI protocols, the participants' subjective perceptions, and image quality. RESULTS: 31 578 participants (97.9%) were successfully included in the MRI study. They reported a high level of comfort and suffered no severe adverse events; mild adverse events occurred in only four participants. Depending on the imaging sequence, the image quality was rated as excellent in 80.2% to 96.8% of cases. Quality assessment with respect to structural features of the brain revealed high consistency across study centers, as well as with regard to age-and sex-based differences in brain volume (men, 1203.81 ± 102.06 cm³; women, 1068.10 ± 86.69 cm³). CONCLUSION: Whole-body MRI was successfully implemented in the NAKO baseline examination and was associated with high patient comfort and very good image quality. The imaging biomarkers of the brain confirmed previously observed differences based on age and sex, underscoring the feasibility of data pooling.

Update on PI-RADS Version 2.1 Diagnostic Performance Benchmarks for Prostate MRI: Systematic Review and Meta-Analysis.

Background Prostate MRI for the detection of clinically significant prostate cancer (csPCa) is standardized by the Prostate Imaging Reporting and Data System (PI-RADS), currently in version 2.1. A systematic review and meta-analysis infrastructure with a 12-month update cycle was established to evaluate the diagnostic performance of PI-RADS over time. Purpose To provide estimates of diagnostic accuracy and cancer detection rates (CDRs) of PI-RADS version 2.1 categories for prostate MRI, which is required for further evidence-based patient management. Materials and Methods A systematic search of PubMed, Embase, Cochrane Library, and multiple trial registers (English-language studies published from March 1, 2019, to August 30, 2022) was performed. Studies that reported data on diagnostic accuracy or CDRs of PI-RADS version 2.1 with csPCa as the primary outcome were included. For the meta-analysis, pooled estimates for sensitivity, specificity, and CDRs were derived from extracted data at the lesion level and patient level. Sensitivity and specificity for PI-RADS greater than or equal to 3 and PI-RADS greater than or equal to 4 considered as test positive were investigated. In addition to individual PI-RADS categories 1-5, subgroup analyses of subcategories (ie, 2+1, 3+0) were performed. Results A total of 70 studies (11 686 lesions, 13 330 patients) were included. At the patient level, with PI-RADS greater than or equal to 3 considered positive, meta-analysis found a 96% summary sensitivity (95% CI: 95, 98) and 43% specificity (95% CI: 33, 54), with an area under the summary receiver operating characteristic (SROC) curve of 0.86 (95% CI: 0.75, 0.93). For PI-RADS greater than or equal to 4, meta-analysis found an 89% sensitivity (95% CI: 85, 92) and 66% specificity (95% CI: 58, 74), with an area under the SROC curve of 0.89 (95% CI: 0.85, 0.92). CDRs were as follows: PI-RADS 1, 6%; PI-RADS 2, 5%; PI-RADS 3, 19%; PI-RADS 4, 54%; and PI-RADS 5, 84%. The CDR was 12% (95% CI: 7, 19) for transition zone 2+1 lesions and 19% (95% CI: 12, 29) for 3+0 lesions (P = .12). Conclusion Estimates of diagnostic accuracy and CDRs for PI-RADS version 2.1 categories are provided for quality benchmarking and to guide further evidence-based patient management. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Tammisetti and Jacobs in this issue.

CT Myocardial Perfusion and CT-FFR versus Invasive FFR for Hemodynamic Relevance of Coronary Artery Disease.

Background CT-derived fractional flow reserve (CT-FFR) and dynamic CT myocardial perfusion imaging enhance the specificity of coronary CT angiography (CCTA) for ruling out coronary artery disease (CAD). However, evidence on comparative diagnostic value remains scarce. Purpose To compare the diagnostic accuracy of CCTA plus CT-FFR, CCTA plus CT perfusion, and sequential CCTA plus CT-FFR and CT perfusion for detecting hemodynamically relevant CAD with that of invasive angiography. Materials and Methods This secondary analysis of a prospective study included patients with chest pain referred for invasive coronary angiography at nine centers from July 2016 to September 2019. CCTA and CT perfusion were performed with third-generation dual-source CT scanners. CT-FFR was assessed on-site. Independent core laboratories analyzed CCTA alone, CCTA plus CT perfusion, CCTA plus CT-FFR, and a sequential approach involving CCTA plus CT-FFR and CT perfusion for the presence of hemodynamically relevant stenosis. Invasive coronary angiography with invasive fractional flow reserve was the reference standard. Diagnostic accuracy metrics and the area under the receiver operating characteristic curve (AUC) were compared with the Sign test and DeLong test. Results Of the 105 participants (mean age, 64 years ± 8 [SD]; 68 male), 49 (47%) had hemodynamically relevant stenoses at invasive coronary angiography. CCTA plus CT-FFR and CCTA plus CT perfusion showed no evidence of a difference for participant-based sensitivities (90% vs 90%, P > .99), specificities (77% vs 79%, P > .99) and vessel-based AUCs (0.84 [95% CI: 0.77, 0.91] vs 0.83 [95% CI: 0.75, 0.91], P = .90). Both had higher participant-based specificity than CCTA alone (54%, both P < .001) without evidence of a difference in sensitivity between CCTA (94%) and CCTA plus CT perfusion (P = .50) or CCTA plus CT-FFR (P = .63). The sequential approach combining CCTA plus CT-FFR with CT perfusion achieved higher participant-based specificity than CCTA plus CT-FFR (88% vs 77%, P = .03) without evidence of a difference in participant-based sensitivity (88% vs 90%, P > .99) and vessel-based AUC (0.85 [95% CI: 0.77, 0.93], P = .78). Compared with CCTA plus CT perfusion, the sequential approach showed no evidence of a difference in participant-based sensitivity (P > .99), specificity (P = .06), or vessel-based AUC (P = .54). Conclusion There was no evidence of a difference in diagnostic accuracy between CCTA plus CT-FFR and CCTA plus CT perfusion for detecting hemodynamically relevant CAD. A sequential approach combining CCTA plus CT-FFR with CT perfusion led to improved participant-based specificity with no evidence of a difference in sensitivity compared with CCTA plus CT-FFR. ClinicalTrials.gov registration no.: NCT02810795 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Sinitsyn in this issue.

[Applications of artificial intelligence in radiology]. / Anwendungsspektrum der künstlichen Intelligenz in der Radiologie.

BACKGROUND: Artificial intelligence (AI) is increasingly finding its way into routine radiological work. OBJECTIVE: Presentation of the current advances and applications of AI along the entire radiological patient journey. METHODS: Systematic literature review of established AI techniques and current research projects, with reference to consensus recommendations. RESULTS: The applications of AI in radiology cover a wide range, starting with AI-supported scheduling and indications assessment, extending to AI-enhanced image acquisition and reconstruction techniques that have the potential to reduce radiation doses in computed tomography (CT) or acquisition times in magnetic resonance imaging (MRI), while maintaining comparable image quality. These include computer-aided detection and diagnosis, such as fracture recognition or nodule detection. Additionally, methods such as worklist prioritization and structured reporting facilitated by large language models enable a rethinking of the reporting process. The use of AI promises to increase the efficiency of all steps of the radiology workflow and an improved diagnostic accuracy. To achieve this, seamless integration into technical workflows and proven evidence of AI systems are necessary. CONCLUSION: Applications of AI have the potential to profoundly influence the role of radiologists in the future.

Deep learning reconstructed T2-weighted Dixon imaging of the spine: Impact on acquisition time and image quality.

PURPOSE: To assess the image quality and impact on acquisition time of a novel deep learning based T2 Dixon sequence (T2DL) of the spine. METHODS: This prospective, single center study included n = 44 consecutive patients with a clinical indication for lumbar MRI at our university radiology department between September 2022 and March 2023. MRI examinations were performed on 1.5-T and 3-T scanners (MAGNETOM Aera and Vida; Siemens Healthineers, Erlangen, Germany) using dedicated spine coils. The MR study protocol consisted of our standard clinical protocol, including a T2 weighted standard Dixon sequence (T2std) and an additional T2DL acquisition. The latter used a conventional sampling pattern with a higher parallel acceleration factor. The individual contrasts acquired for Dixon water-fat separation were then reconstructed using a dedicated research application. After reconstruction of the contrast images from k-space data, a conventional water-fat separation was performed to provide derived water images. Two readers with 6 and 4 years of experience in interpreting MSK imaging, respectively, analyzed the images in a randomized fashion. Regarding overall image quality, banding artifacts, artifacts, sharpness, noise, and diagnostic confidence were analyzed using a 5-point Likert scale (from 1 = non-diagnostic to 5 = excellent image quality). Statistical analyses included the Wilcoxon signed-rank test and weighted Cohen's kappa statistics. RESULTS: Forty-four patients (mean age 53 years (±18), male sex: 39 %) were prospectively included. Thirty-one examinations were performed on 1.5 T and 13 examinations on 3 T scanners. A sequence was successfully acquired in all patients. The total acquisition time of T2DL was 93 s at 1.5-T and 86 s at 3-T, compared to 235 s, and 257 s, respectively for T2std (reduction of acquisition time: 60.4 % at 1.5-T, and 66.5 % at 3-T; p < 0.01). Overall image quality was rated equal for both sequences (median T2DL: 5[3 -5], and median T2std: 5 [2 -5]; p = 0.57). T2DL showed significantly reduced noise levels compared to T2std (5 [4 -5] versus 4 [3 -4]; p < 0.001). In addition, sharpness was rated to be significantly higher in T2DL (5 [4 -5] versus 4 [3 -5]; p < 0.001). Although T2DL displayed significantly more banding artifacts (5 [2 -5] versus 5 [4 -5]; p < 0.001), no significant impact on readers diagnostic confidence between sequences was noted (T2std: 5 [2 -5], and T2DL: 5 [3 -5]; p = 0.61). Substantial inter-reader and intrareader agreement was observed for T2DL overall image quality (κ: 0.77, and κ: 0.8, respectively). CONCLUSION: T2DL is feasible, yields an image quality comparable to the reference standard while substantially reducing the acquisition time.

Assessing bolus peak position in CT perfusion: High variance persisting despite age-dependency in a large cohort.

PURPOSE: CT perfusion (CTP) is a valuable tool in suspected acute ischemic stroke. A substantial variability of the delay between contrast injection and bolus arrival in the brain is conceivable. We investigated the distribution of the peak positions of the concentration time curves measured in an artery (arterial input function, AIF) and - in cases with ischemia - also measured in the penumbra. METHODS: We report on 2624 perfusion scans (52 % female, mean age 72.2 ± 14.4 years) with stroke present in 1636 cases. From the attenuation time curves of the AIF and the penumbra, we calculated the respective bolus peak positions and investigated the distribution of the peak positions. Further, we analyzed the bolus peak positions for associations with age. RESULTS: The bolus peaked significantly later in older patients, both in the AIF and in the penumbra (all p < 0.001). In the whole cohort, we found a significant association of age with the bolus peak position of the AIF (ρ = 0.334; p < 0.001). In patients with stroke, age was also associated to the peak position of the AIF (ρ = 0.305; p < 0.001), and the penumbra (ρ = 0.246, p < 0.001). However, a substantial range of peak positions of the AIF and penumbra was noted across all age ranges. CONCLUSIONS: This study revealed a strong age-dependency of the contrast bolus arrival in both healthy and ischemic tissue. This variability makes non-uniform sampling schemes, which have been suggested to reduce radiation dose, problematic, as they might not always optimally capture the bolus in all cases.

T1ρ relaxation mapping in osteochondral lesions of the talus: a non-invasive biomarker for altered biomechanical properties of hyaline cartilage?

BACKGROUND: To evaluate T1ρ relaxation mapping in patients with symptomatic talar osteochondral lesions (OLT) and healthy controls (HC) at rest, with axial loading and traction. METHODS: Participants underwent 3-T ankle magnetic resonance imaging at rest and with 500 N loading and 120 N traction, without axial traction for a subcohort of 17/29 HC. We used a fast low-angle shot sequence with variable spin-lock intervals for monoexponential T1ρ fitting. Cartilage was manually segmented to extract T1ρ values. RESULTS: We studied 29 OLT patients (age 31.7 ± 7.5 years, 15 females, body mass index [BMI] 25.0 ± 3.4 kg/m2) and 29 HC (age 25.2 ± 4.3 years, 17 females, BMI 22.5 ± 2.3 kg/m2. T1ρ values of OLT (50.4 ± 3.4 ms) were higher than those of intact cartilage regions of OLT patients (47.2 ± 3.4 ms; p = 0.003) and matched HC cartilage (48.1 ± 3.3 ms; p = 0.030). Axial loading and traction induced significant T1ρ changes in the intact cartilage regions of patients (loading, mean difference -1.1 ms; traction, mean difference 1.4 ms; p = 0.030 for both) and matched HC cartilage (-2.2 ms, p = 0.003; 2.3 ms, p = 0.030; respectively), but not in the OLT itself (-1.3 ms; p = 0.150; +1.9 ms; p = 0.150; respectively). CONCLUSION: Increased T1ρ values may serve as a biomarker of cartilage degeneration in OLT. The absence of load- and traction-induced T1ρ changes in OLT compared to intact cartilage suggests that T1ρ may reflect altered biomechanical properties of hyaline cartilage. TRIAL REGISTRATION: DRKS, DRKS00024010. Registered 11 January 2021, https://drks.de/search/de/trial/DRKS00024010 . RELEVANCE STATEMENT: T1ρ mapping has the potential to evaluate compositional and biomechanical properties of the talar cartilage and may improve therapeutic decision-making in patients with osteochondral lesions. KEY POINTS: T1ρ values in osteochondral lesions increased compared to intact cartilage. Significant load- and traction-induced T1ρ changes were observed in visually intact regions and in healthy controls but not in osteochondral lesions. T1ρ may serve as an imaging biomarker for biomechanical properties of cartilage.

Associations between adrenal gland volume and adipose tissue compartments - a whole body MRI study.

BACKGROUND: Obesity is associated with alterations in the hypothalamic-pituitary-adrenal (HPA) axis. Effects of glucocorticoids on adipose tissues appear to depend on the specific adipose depot, in which they take place. In this study, we aimed to investigate the role of MRI-based adrenal gland volume as an imaging marker in association with different adipose tissue compartments. METHODS: The study cohort derives from the population-based research platform KORA (Cooperative Health Research in the Augsburg Region, Germany) MRI sub-study, a cross-sectional sub-study investigating the interactions between subclinical metabolic changes and cardiovascular disease in a study sample of 400 participants. Originally, eligible subjects underwent a whole-body MRI. MRI-based segmentations were performed manually and semi-automatically for adrenal gland volume, visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), epi- and pericardial fat and renal sinus fat. Hepatic and pancreatic lipid content were measured as pancreatic proton density fraction (PDFF) and MR-spectroscopic hepatic fat fraction (HFF). Multivariable linear regression analyses were performed. RESULTS: A number of 307 participants (56.2 ± 9.1 years, 60.3% male, 14.3% with type 2 diabetes (T2DM), 30.6% with obesity, 34.2% with hypertension) were included. In multivariable analyses, strong positive associations between adrenal gland volume and VAT, total adipose tissue (TAT) as well as HFF persisted after extensive step-wise adjustment for possible metabolic confounders (VAT: beta = 0.31, 95%-CI [0.71, 0.81], p < 0.001; TAT: beta = 0.14, 95%-CI [0.06, 0.23], p < 0.001; HFF: beta = 1.17, 95%-CI [1.04, 1.31], p = 0.009). In contrast, associations between adrenal gland volume and SAT were attenuated in multivariate analysis after adjusting for BMI. Associations between pancreatic PDFF, epi- and pericardial fat and renal sinus fat were mediated to a great extent by VAT (pancreatic PDFF: 72%, epicardial adipose tissue: 100%, pericardial adipose tissue: 100%, renal sinus fat: 81.5%). CONCLUSION: Our results found MRI-based adrenal gland volume as a possible imaging biomarker of unfavorable adipose tissue distribution, irrespective of metabolic risk factors. Thus, adrenal gland volume may serve as a potential MRI-based biomarker of metabolic changes and contributes to an individual characterization of metabolic states and individual risk stratification. Future studies should elucidate in a longitudinal study design, if and how HPA axis activation may trigger unfavorable adipose tissue distribution and whether and to which extent this is involved in the pathogenesis of manifest metabolic syndrome.

Association between myosteatosis and impaired glucose metabolism: A deep learning whole-body magnetic resonance imaging population phenotyping approach.

BACKGROUND: There is increasing evidence that myosteatosis, which is currently not assessed in clinical routine, plays an important role in risk estimation in individuals with impaired glucose metabolism, as it is associated with the progression of insulin resistance. With advances in artificial intelligence, automated and accurate algorithms have become feasible to fill this gap. METHODS: In this retrospective study, we developed and tested a fully automated deep learning model using data from two prospective cohort studies (German National Cohort [NAKO] and Cooperative Health Research in the Region of Augsburg [KORA]) to quantify myosteatosis on whole-body T1-weighted Dixon magnetic resonance imaging as (1) intramuscular adipose tissue (IMAT; the current standard) and (2) quantitative skeletal muscle (SM) fat fraction (SMFF). Subsequently, we investigated the two measures for their discrimination of and association with impaired glucose metabolism beyond baseline demographics (age, sex and body mass index [BMI]) and cardiometabolic risk factors (lipid panel, systolic blood pressure, smoking status and alcohol consumption) in asymptomatic individuals from the KORA study. Impaired glucose metabolism was defined as impaired fasting glucose or impaired glucose tolerance (140-200 mg/dL) or prevalent diabetes mellitus. RESULTS: Model performance was high, with Dice coefficients of ≥0.81 for IMAT and ≥0.91 for SM in the internal (NAKO) and external (KORA) testing sets. In the target population (380 KORA participants: mean age of 53.6 ± 9.2 years, BMI of 28.2 ± 4.9 kg/m2, 57.4% male), individuals with impaired glucose metabolism (n = 146; 38.4%) were older and more likely men and showed a higher cardiometabolic risk profile, higher IMAT (4.5 ± 2.2% vs. 3.9 ± 1.7%) and higher SMFF (22.0 ± 4.7% vs. 18.9 ± 3.9%) compared to normoglycaemic controls (all P ≤ 0.005). SMFF showed better discrimination for impaired glucose metabolism than IMAT (area under the receiver operating characteristic curve [AUC] 0.693 vs. 0.582, 95% confidence interval [CI] [0.06-0.16]; P < 0.001) but was not significantly different from BMI (AUC 0.733 vs. 0.693, 95% CI [-0.09 to 0.01]; P = 0.15). In univariable logistic regression, IMAT (odds ratio [OR] = 1.18, 95% CI [1.06-1.32]; P = 0.004) and SMFF (OR = 1.19, 95% CI [1.13-1.26]; P < 0.001) were associated with a higher risk of impaired glucose metabolism. This signal remained robust after multivariable adjustment for baseline demographics and cardiometabolic risk factors for SMFF (OR = 1.10, 95% CI [1.01-1.19]; P = 0.028) but not for IMAT (OR = 1.14, 95% CI [0.97-1.33]; P = 0.11). CONCLUSIONS: Quantitative SMFF, but not IMAT, is an independent predictor of impaired glucose metabolism, and discrimination is not significantly different from BMI, making it a promising alternative for the currently established approach. Automated methods such as the proposed model may provide a feasible option for opportunistic screening of myosteatosis and, thus, a low-cost personalized risk assessment solution.

Radiomics workflow definition & challenges - German priority program 2177 consensus statement on clinically applied radiomics.

OBJECTIVES: Achieving a consensus on a definition for different aspects of radiomics workflows to support their translation into clinical usage. Furthermore, to assess the perspective of experts on important challenges for a successful clinical workflow implementation. MATERIALS AND METHODS: The consensus was achieved by a multi-stage process. Stage 1 comprised a definition screening, a retrospective analysis with semantic mapping of terms found in 22 workflow definitions, and the compilation of an initial baseline definition. Stages 2 and 3 consisted of a Delphi process with over 45 experts hailing from sites participating in the German Research Foundation (DFG) Priority Program 2177. Stage 2 aimed to achieve a broad consensus for a definition proposal, while stage 3 identified the importance of translational challenges. RESULTS: Workflow definitions from 22 publications (published 2012-2020) were analyzed. Sixty-nine definition terms were extracted, mapped, and semantic ambiguities (e.g., homonymous and synonymous terms) were identified and resolved. The consensus definition was developed via a Delphi process. The final definition comprising seven phases and 37 aspects reached a high overall consensus (> 89% of experts "agree" or "strongly agree"). Two aspects reached no strong consensus. In addition, the Delphi process identified and characterized from the participating experts' perspective the ten most important challenges in radiomics workflows. CONCLUSION: To overcome semantic inconsistencies between existing definitions and offer a well-defined, broad, referenceable terminology, a consensus workflow definition for radiomics-based setups and a terms mapping to existing literature was compiled. Moreover, the most relevant challenges towards clinical application were characterized. CRITICAL RELEVANCE STATEMENT: Lack of standardization represents one major obstacle to successful clinical translation of radiomics. Here, we report a consensus workflow definition on different aspects of radiomics studies and highlight important challenges to advance the clinical adoption of radiomics. KEY POINTS: Published radiomics workflow terminologies are inconsistent, hindering standardization and translation. A consensus radiomics workflow definition proposal with high agreement was developed. Publicly available result resources for further exploitation by the scientific community.

MRI-based adrenal gland volume is associated with cardiovascular alterations in individuals without prior cardiovascular disease.

Aim of this study was to analyse the associations of cardiovascular health and adrenal gland volume as a rather new imaging biomarker of chronic hypothalamic-pituitary-adrenal (HPA) axis activation. The study population originates from the KORA population-based cross-sectional prospective cohort. 400 participants without known cardiovascular disease underwent a whole-body MRI. Manual segmentation of adrenal glands was performed on VIBE-Dixon gradient-echo sequence. MRI based evaluation of cardiac parameters was achieved semi-automatically. Cardiometabolic risk factors were obtained through standardized interviews and medical examination. Univariate and multivariate associations were derived. Bi-directional causal mediation analysis was performed. 351 participants were eligible for analysis (56 ± 9.1 years, male 58.7%). In multivariate analysis, significant associations were observed between adrenal gland volume and hypertension (outcome hypertension: Odds Ratio = 1.11, 95% CI [1.01, 1.21], p = 0.028), left ventricular remodelling index (LVRI) (outcome LVRI: ß = 0.01, 95% CI [0.00, 0.02], p = 0.011), and left ventricular (LV) wall thickness (outcome LV wall thickness: ß = 0.06, 95% CI [0.02, 0.09], p = 0.005). In bi-directional causal mediation analysis adrenal gland volume had a borderline significant mediating effect on the association between hypertension and LVRI (p = 0.052) as well as wall thickness (p = 0.054). MRI-based assessment of adrenal gland enlargement is associated with hypertension and LV remodelling. Adrenal gland volume may serve as an indirect cardiovascular imaging biomarker.

Myosteatosis in multiple myeloma: a key determinant of survival beyond sarcopenia.

OBJECTIVE: Fatty infiltration of skeletal muscle (Myosteatosis) is associated with increased frailty, decreased muscle and mobility function, which seems fairly prevalent in multiple myeloma (MM) patients. This study aimed to determine the prognostic value of myosteatosis assessed by CT for progression-free survival (PFS) and overall survival (OS). MATERIALS AND METHODS: This IRB-approved cohort study included patients with newly diagnosed MM who were treated at a single university hospital and received CT at baseline. Geriatric assessment was performed via International Myeloma Working Group frailty score and Revised Myeloma Comorbidity Index. Myosteatosis was determined through measurement of paravertebral muscle radiodensity. Statistical analyses included uni- and multivariable Cox proportional hazard models and the Kaplan-Meier-method. RESULTS: A total of 226 newly diagnosed MM patients (median age: 65 years [range: 29-89], 63% males, mean BMI: 25 [14-42]) were analyzed. The prevalence of myosteatosis was 51%. Muscle radiodensity was significantly decreased in individuals with International Staging System stage III vs. I (p < 0.001), indicating higher fatty muscle infiltration in patients with advanced disease. Both PFS and OS were significantly decreased in patients with myosteatosis (PFS: median 32.0 months (95% CI 20.5.5-42.2) vs. 66.4 months without myosteatosis (95% CI 42.5-not reached), p < .001); OS: median 58.6 (95% CI 51.3-90.2) vs. not reached, p < .001). Myosteatosis remained an independent predictor of OS in multivariable analyses (HR: 1.98; 95%-CI: 1.20-3.27). CONCLUSION: Myosteatosis seems fairly prevalent in patients with newly diagnosed MM and associated with impaired overall survival. Prospective clinical trials are required to better understand the role of myosteatosis in MM patients.

Accuracy of a deep learning-based algorithm for the detection of thoracic aortic calcifications in chest computed tomography and cardiovascular surgery planning.

OBJECTIVES: To assess the accuracy of a deep learning-based algorithm for fully automated detection of thoracic aortic calcifications in chest computed tomography (CT) with a focus on the aortic clamping zone. METHODS: We retrospectively included 100 chest CT scans from 91 patients who were examined on second- or third-generation dual-source scanners. Subsamples comprised 47 scans with an electrocardiogram-gated aortic angiography and 53 unenhanced scans. A deep learning model performed aortic landmark detection and aorta segmentation to derive 8 vessel segments. Associated calcifications were detected and their volumes measured using a mean-based density thresholding. Algorithm parameters (calcium cluster size threshold, aortic mask dilatation) were varied to determine optimal performance for the upper ascending aorta that encompasses the aortic clamping zone. A binary visual rating served as a reference. Standard estimates of diagnostic accuracy and inter-rater agreement using Cohen's Kappa were calculated. RESULTS: Thoracic aortic calcifications were observed in 74% of patients with a prevalence of 27-70% by aorta segment. Using different parameter combinations, the algorithm provided binary ratings for all scans and segments. The best performing parameter combination for the presence of calcifications in the aortic clamping zone yielded a sensitivity of 93% and a specificity of 82%, with an area under the receiver operating characteristic curve of 0.874. Using these parameters, the inter-rater agreement ranged from κ 0.66 to 0.92 per segment. CONCLUSIONS: Fully automated segmental detection of thoracic aortic calcifications in chest CT performs with high accuracy. This includes the critical preoperative assessment of the aortic clamping zone.

Cardioprotective effects of vaccination in hospitalized patients with COVID-19.

COVID-19 vaccination has been shown to prevent and reduce the severity of COVID-19 disease. The aim of this study was to explore the cardioprotective effect of COVID-19 vaccination in hospitalized COVID-19 patients. In this retrospective, single-center cohort study, we included hospitalized COVID-19 patients with confirmed vaccination status from July 2021 to February 2022. We assessed outcomes such as acute cardiac events and cardiac biomarker levels through clinical and laboratory data. Our analysis covered 167 patients (69% male, mean age 58 years, 42% being fully vaccinated). After adjustment for confounders, vaccinated hospitalized COVID-19 patients displayed a reduced relative risk for acute cardiac events (RR: 0.33, 95% CI [0.07; 0.75]) and showed diminished troponin T levels (Cohen's d: - 0.52, 95% CI [- 1.01; - 0.14]), compared to their non-vaccinated peers. Type 2 diabetes (OR: 2.99, 95% CI [1.22; 7.35]) and existing cardiac diseases (OR: 4.31, 95% CI [1.83; 10.74]) were identified as significant risk factors for the emergence of acute cardiac events. Our findings suggest that COVID-19 vaccination may confer both direct and indirect cardioprotective effects in hospitalized COVID-19 patients.

A retrieval-augmented chatbot based on GPT-4 provides appropriate differential diagnosis in gastrointestinal radiology: a proof of concept study.

BACKGROUND: We investigated the potential of an imaging-aware GPT-4-based chatbot in providing diagnoses based on imaging descriptions of abdominal pathologies. METHODS: Utilizing zero-shot learning via the LlamaIndex framework, GPT-4 was enhanced using the 96 documents from the Radiographics Top 10 Reading List on gastrointestinal imaging, creating a gastrointestinal imaging-aware chatbot (GIA-CB). To assess its diagnostic capability, 50 cases on a variety of abdominal pathologies were created, comprising radiological findings in fluoroscopy, MRI, and CT. We compared the GIA-CB to the generic GPT-4 chatbot (g-CB) in providing the primary and 2 additional differential diagnoses, using interpretations from senior-level radiologists as ground truth. The trustworthiness of the GIA-CB was evaluated by investigating the source documents as provided by the knowledge-retrieval mechanism. Mann-Whitney U test was employed. RESULTS: The GIA-CB demonstrated a high capability to identify the most appropriate differential diagnosis in 39/50 cases (78%), significantly surpassing the g-CB in 27/50 cases (54%) (p = 0.006). Notably, the GIA-CB offered the primary differential in the top 3 differential diagnoses in 45/50 cases (90%) versus g-CB with 37/50 cases (74%) (p = 0.022) and always with appropriate explanations. The median response time was 29.8 s for GIA-CB and 15.7 s for g-CB, and the mean cost per case was $0.15 and $0.02, respectively. CONCLUSIONS: The GIA-CB not only provided an accurate diagnosis for gastrointestinal pathologies, but also direct access to source documents, providing insight into the decision-making process, a step towards trustworthy and explainable AI. Integrating context-specific data into AI models can support evidence-based clinical decision-making. RELEVANCE STATEMENT: A context-aware GPT-4 chatbot demonstrates high accuracy in providing differential diagnoses based on imaging descriptions, surpassing the generic GPT-4. It provided formulated rationale and source excerpts supporting the diagnoses, thus enhancing trustworthy decision-support. KEY POINTS: • Knowledge retrieval enhances differential diagnoses in a gastrointestinal imaging-aware chatbot (GIA-CB). • GIA-CB outperformed the generic counterpart, providing formulated rationale and source excerpts. • GIA-CB has the potential to pave the way for AI-assisted decision support systems.

Ectopic pancreas tissue in the posterior mediastinum.

The occurrence of ectopic pancreas in the mediastinum is rare. Herein, we report a 22-year-old female who presented with right shoulder pain, dysphagia, fever and headaches. Chest computer tomography revealed a mass in the posterior mediastinum with accompanying signs of acute mediastinitis. Needle biopsy and fine-needle aspiration revealed ectopic gastral tissue and ectopic pancreas tissue, respectively. Surgical resection was attempted due to recurring acute pancreatitis episodes. However, due to chronic-inflammatory adhesions of the mass to the tracheal wall, en-bloc resection was not possible without major tracheal resection. Since then, recurring pancreatitis episodes have been treated conservatively with antibiotics. We report this case due to its differing clinical and radiological findings in comparison to previous case reports, none of which pertained a case of ectopic pancreas tissue in the posterior mediastinum with recurring acute pancreatitis and mediastinitis.

Photon-counting detector computed tomography for metal artifact reduction: a comparative study of different artifact reduction techniques in patients with orthopedic implants.

PURPOSE: Artifacts caused by metallic implants remain a challenge in computed tomography (CT). We investigated the impact of photon-counting detector computed tomography (PCD-CT) for artifact reduction in patients with orthopedic implants with respect to image quality and diagnostic confidence using different artifact reduction approaches. MATERIAL AND METHODS: In this prospective study, consecutive patients with orthopedic implants underwent PCD-CT imaging of the implant area. Four series were reconstructed for each patient (clinical standard reconstruction [PCD-CTStd], monoenergetic images at 140 keV [PCD-CT140keV], iterative metal artifact reduction (iMAR) corrected [PCD-CTiMAR], combination of iMAR and 140 keV monoenergetic [PCD-CT140keV+iMAR]). Subsequently, three radiologists evaluated the reconstructions in a random and blinded manner for image quality, artifact severity, anatomy delineation (adjacent and distant), and diagnostic confidence using a 5-point Likert scale (5 = excellent). In addition, the coefficient of variation [CV] and the relative quantitative artifact reduction potential were obtained as objective measures. RESULTS: We enrolled 39 patients with a mean age of 67.3 ± 13.2 years (51%; n = 20 male) and a mean BMI of 26.1 ± 4 kg/m2. All image quality measures and diagnostic confidence were significantly higher for the iMAR vs. non-iMAR reconstructions (all p < 0.001). No significant effect of the different artifact reduction approaches on CV was observed (p = 0.26). The quantitative analysis indicated the most effective artifact reduction for the iMAR reconstructions, which was higher than PCD-CT140keV (p < 0.001). CONCLUSION: PCD-CT allows for effective metal artifact reduction in patients with orthopedic implants, resulting in superior image quality and diagnostic confidence with the potential to improve patient management and clinical decision making.

Association Between Body Composition and Survival in Patients With Gastroesophageal Adenocarcinoma: An Automated Deep Learning Approach.

PURPOSE: Body composition (BC) may play a role in outcome prognostication in patients with gastroesophageal adenocarcinoma (GEAC). Artificial intelligence provides new possibilities to opportunistically quantify BC from computed tomography (CT) scans. We developed a deep learning (DL) model for fully automatic BC quantification on routine staging CTs and determined its prognostic role in a clinical cohort of patients with GEAC. MATERIALS AND METHODS: We developed and tested a DL model to quantify BC measures defined as subcutaneous and visceral adipose tissue (VAT) and skeletal muscle on routine CT and investigated their prognostic value in a cohort of patients with GEAC using baseline, 3-6-month, and 6-12-month postoperative CTs. Primary outcome was all-cause mortality, and secondary outcome was disease-free survival (DFS). Cox regression assessed the association between (1) BC at baseline and mortality and (2) the decrease in BC between baseline and follow-up scans and mortality/DFS. RESULTS: Model performance was high with Dice coefficients ≥0.94 ± 0.06. Among 299 patients with GEAC (age 63.0 ± 10.7 years; 19.4% female), 140 deaths (47%) occurred over a median follow-up of 31.3 months. At baseline, no BC measure was associated with DFS. Only a substantial decrease in VAT >70% after a 6- to 12-month follow-up was associated with mortality (hazard ratio [HR], 1.99 [95% CI, 1.18 to 3.34]; P = .009) and DFS (HR, 1.73 [95% CI, 1.01 to 2.95]; P = .045) independent of age, sex, BMI, Union for International Cancer Control stage, histologic grading, resection status, neoadjuvant therapy, and time between surgery and follow-up CT. CONCLUSION: DL enables opportunistic estimation of BC from routine staging CT to quantify prognostic information. In patients with GEAC, only a substantial decrease of VAT 6-12 months postsurgery was an independent predictor for DFS beyond traditional risk factors, which may help to identify individuals at high risk who go otherwise unnoticed.