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.

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.

Extracranial Vascular Anomalies Driven by RAS/MAPK Variants: Spectrum and Genotype-Phenotype Correlations.

BACKGROUND: We aimed to correlate alterations in the rat sarcoma virus (RAS)/mitogen-activated protein kinase pathway in vascular anomalies to the clinical phenotype for improved patient and treatment stratification. METHODS AND RESULTS: This retrospective multicenter cohort study included 29 patients with extracranial vascular anomalies containing mosaic pathogenic variants (PVs) in genes of the RAS/mitogen-activated protein kinase pathway. Tissue samples were collected during invasive treatment or clinically indicated biopsies. PVs were detected by the targeted sequencing of panels of genes known to be associated with vascular anomalies, performed using DNA from affected tissue. Subgroup analyses were performed according to the affected genes with regard to phenotypic characteristics in a descriptive manner. Twenty-five vascular malformations, 3 vascular tumors, and 1 patient with both a vascular malformation and vascular tumor presented the following distribution of PVs in genes: Kirsten rat sarcoma viral oncogene (n=10), neuroblastoma ras viral oncogene homolog (n=1), Harvey rat sarcoma viral oncogene homolog (n=5), V-Raf murine sarcoma viral oncogene homolog B (n=8), and mitogen-activated protein kinase kinase 1 (n=5). Patients with RAS PVs had advanced disease stages according to the Schobinger classification (stage 3-4: RAS, 9/13 versus non-RAS, 3/11) and more frequent progression after treatment (RAS, 10/13 versus non-RAS, 2/11). Lesions with Kirsten rat sarcoma viral oncogene PVs infiltrated more tissue layers compared with the other PVs including other RAS PVs (multiple tissue layers: Kirsten rat sarcoma viral oncogene, 8/10 versus other PVs, 6/19). CONCLUSIONS: This comparison of patients with various PVs in genes of the RAS/MAPK pathway provides potential associations with certain morphological and clinical phenotypes. RAS variants were associated with more aggressive phenotypes, generating preliminary data and hypothesis for future larger studies.

Deep Learning to Estimate Cardiovascular Risk From Chest Radiographs : A Risk Prediction Study.

BACKGROUND: Guidelines for primary prevention of atherosclerotic cardiovascular disease (ASCVD) recommend a risk calculator (ASCVD risk score) to estimate 10-year risk for major adverse cardiovascular events (MACE). Because the necessary inputs are often missing, complementary approaches for opportunistic risk assessment are desirable. OBJECTIVE: To develop and test a deep-learning model (CXR CVD-Risk) that estimates 10-year risk for MACE from a routine chest radiograph (CXR) and compare its performance with that of the traditional ASCVD risk score for implications for statin eligibility. DESIGN: Risk prediction study. SETTING: Outpatients potentially eligible for primary cardiovascular prevention. PARTICIPANTS: The CXR CVD-Risk model was developed using data from a cancer screening trial. It was externally validated in 8869 outpatients with unknown ASCVD risk because of missing inputs to calculate the ASCVD risk score and in 2132 outpatients with known risk whose ASCVD risk score could be calculated. MEASUREMENTS: 10-year MACE predicted by CXR CVD-Risk versus the ASCVD risk score. RESULTS: Among 8869 outpatients with unknown ASCVD risk, those with a risk of 7.5% or higher as predicted by CXR CVD-Risk had higher 10-year risk for MACE after adjustment for risk factors (adjusted hazard ratio [HR], 1.73 [95% CI, 1.47 to 2.03]). In the additional 2132 outpatients with known ASCVD risk, CXR CVD-Risk predicted MACE beyond the traditional ASCVD risk score (adjusted HR, 1.88 [CI, 1.24 to 2.85]). LIMITATION: Retrospective study design using electronic medical records. CONCLUSION: On the basis of a single CXR, CXR CVD-Risk predicts 10-year MACE beyond the clinical standard and may help identify individuals at high risk whose ASCVD risk score cannot be calculated because of missing data. PRIMARY FUNDING SOURCE: None.

Value of virtual non-contrast images to identify uncomplicated cystic renal lesions: photon-counting detector CT vs. dual-energy integrating detector CT.

PURPOSE: To investigate the value of photon-counting detector CT (PCD-CT) derived virtual non-contrast (VNC) reconstructions to identify renal cysts in comparison with conventional dual-energy integrating detector (DE EID) CT-derived VNC reconstructions. MATERIAL AND METHODS: We prospectively enrolled consecutive patients with simple renal cysts (Bosniak classification-Version 2019, density ≤ 20 HU and/or enhancement ≤ 20 HU) who underwent multiphase (non-contrast, arterial, portal venous phase) PCD-CT and for whom non-contrast and portal venous phase DE EID-CT was available. Subsequently, VNC reconstructions were calculated for all contrast phases and density as well as contrast enhancement within the cysts were measured and compared. MRI and/or ultrasound served as reference standards for lesion classification. RESULTS: 19 patients (1 cyst per patient; age 69.5 ± 10.7 years; 17 [89.5%] male) were included. Density measurements on PCD-CT non-contrast and VNC reconstructions (arterial and portal venous phase) revealed no significant effect on HU values (p = 0.301). In contrast, a significant difference between non-contrast vs. VNC images was found for DE EID-CT (p = 0.02). For PCD-CT, enhancement for VNC reconstructions was < 20 HU for all evaluated cysts. DE EID-CT measurements revealed an enhancement of > 20 HU in five lesions (26.3%) using the VNC reconstructions, which was not seen with the non-contrast images. CONCLUSION: PCD-CT-derived VNC images allow for reliable and accurate characterization of simple cystic renal lesions similar to non-contrast scans whereas VNC images calculated from DE EID-CT resulted in substantial false characterization. Thus, PCD-CT-derived VNC images may substitute for non-contrast images and reduce radiation dose and follow-up imaging.

Ultra-high-resolution photon-counting detector CT in evaluating coronary stent patency: a comparison to invasive coronary angiography.

OBJECTIVES: To determine the diagnostic accuracy of ultra-high-resolution photon-counting detector CT angiography (UHR PCD-CTA) for evaluating coronary stent patency compared to invasive coronary angiography (ICA). METHODS: Consecutive, clinically referred patients with prior coronary stent implantation were prospectively enrolled between August 2022 and March 2023 and underwent UHR PCD-CTA (collimation, 120 × 0.2 mm). Two radiologists independently analyzed image quality of the in-stent lumen using a 5-point Likert scale, ranging from 1 ("excellent") to 5 ("non-diagnostic"), and assessed all coronary stents for the presence of in-stent stenosis (≥ 50% lumen narrowing). The diagnostic accuracy of UHR PCD-CTA was determined, with ICA serving as the standard of reference. RESULTS: A total of 44 coronary stents in 18 participants (mean age, 83 years ± 6 [standard deviation]; 12 women) were included in the analysis. In 3/44 stents, both readers described image quality as non-diagnostic, whereas reader 2 noted a fourth stent to have non-diagnostic image quality. In comparison to ICA, UHR PCD-CTA demonstrated a sensitivity, specificity, and accuracy of 100% (95% CI [confidence interval] 47.8, 100), 92.3% (95% CI 79.1, 98.4), and 93.2% (95% CI 81.3, 98.6) for reader 1 and 100% (95% CI 47.8, 100), 87.2% (95% CI 72.6, 95.7), and 88.6% (95% CI 75.4, 96.2) for reader 2, respectively. Both readers observed a 100% negative predictive value (36/36 stents and 34/34 stents). Stent patency inter-reader agreement was 90.1%, corresponding to a substantial Cohen's kappa value of 0.72. CONCLUSIONS: UHR PCD-CTA enables non-invasive assessment of coronary stent patency with high image quality and diagnostic accuracy. CLINICAL RELEVANCE STATEMENT: Ultra-high-resolution photon-counting detector CT angiography represents a reliable and non-invasive method for assessing coronary stent patency. Its high negative predictive value makes it a promising alternative over invasive coronary angiography for the rule-out of in-stent stenosis. KEY POINTS: • CT-based evaluation of coronary stent patency is limited by stent-induced artifacts and spatial resolution. • Ultra-high-resolution photon-counting detector CT accurately evaluates coronary stent patency compared to invasive coronary angiography. • Photon-counting detector CT represents a promising method for the non-invasive rule-out of in-stent stenosis.

Deep learning to estimate impaired glucose metabolism from Magnetic Resonance Imaging of the liver: An opportunistic population screening approach.

AIM: Diabetes is a global health challenge, and many individuals are undiagnosed and not aware of their increased risk of morbidity/mortality although dedicated tests are available, which indicates the need for novel population-wide screening approaches. Here, we developed a deep learning pipeline for opportunistic screening of impaired glucose metabolism using routine magnetic resonance imaging (MRI) of the liver and tested its prognostic value in a general population setting. METHODS: In this retrospective study a fully automatic deep learning pipeline was developed to quantify liver shape features on routine MR imaging using data from a prospective population study. Subsequently, the association between liver shape features and impaired glucose metabolism was investigated in individuals with prediabetes, type 2 diabetes and healthy controls without prior cardiovascular diseases. K-medoids clustering (3 clusters) with a dissimilarity matrix based on Euclidean distance and ordinal regression was used to assess the association between liver shape features and glycaemic status. RESULTS: The deep learning pipeline showed a high performance for liver shape analysis with a mean Dice score of 97.0±0.01. Out of 339 included individuals (mean age 56.3±9.1 years; males 58.1%), 79 (23.3%) and 46 (13.6%) were classified as having prediabetes and type 2 diabetes, respectively. Individuals in the high risk cluster using all liver shape features (n = 14) had a 2.4 fold increased risk of impaired glucose metabolism after adjustment for cardiometabolic risk factors (age, sex, BMI, total cholesterol, alcohol consumption, hypertension, smoking and hepatic steatosis; OR 2.44 [95% CI 1.12-5.38]; p = 0.03). Based on individual shape features, the strongest association was found between liver volume and impaired glucose metabolism after adjustment for the same risk factors (OR 1.97 [1.38-2.85]; p<0.001). CONCLUSIONS: Deep learning can estimate impaired glucose metabolism on routine liver MRI independent of cardiometabolic risk factors and hepatic steatosis.

Improved early outcome prediction by MRI-based 3D tumor volume assessment in patients with CNS lymphomas.

BACKGROUND: Central nervous system lymphomas (CNSL) display remarkable clinical heterogeneity, yet accurate prediction of outcomes remains challenging. The IPCG criteria are widely used in routine practice for the assessment of treatment response. However, the value of the IPCG criteria for ultimate outcome prediction is largely unclear, mainly due to the uncertainty in delineating complete from partial responses during and after treatment. METHODS: We explored various MRI features including semi-automated 3D tumor volume measurements at different disease milestones and their association with survival in 93 CNSL patients undergoing curative-intent treatment. RESULTS: At diagnosis, patients with more than 3 lymphoma lesions, periventricular involvement, and high 3D tumor volumes showed significantly unfavorable PFS and OS. At first interim MRI during treatment, the IPCG criteria failed to discriminate outcomes in responding patients. Therefore, we randomized these patients into training and validation cohorts to investigate whether 3D tumor volumetry could improve outcome prediction. We identified a 3D tumor volume reduction of ≥97% as the optimal threshold for risk stratification (=3D early response, 3D_ER). Applied to the validation cohort, patients achieving 3D_ER had significantly superior outcomes. In multivariate analyses, 3D_ER was independently prognostic of PFS and OS. Finally, we leveraged prognostic information from 3D MRI features and circulating biomarkers to build a composite metric that further improved outcome prediction in CNSL. CONCLUSIONS: We developed semi-automated 3D tumor volume measurements as strong and independent early predictors of clinical outcomes in CNSL patients. These radiologic features could help improve risk stratification and help guide future treatment approaches.

Photon-counting computed tomography - clinical application in oncological, cardiovascular, and pediatric radiology. / Photon-Counting Computertomographie ­ klinische Anwendungen in der onkologischen, kardiovaskulären und pädiatrischen Radiologie.

BACKGROUND: Photon-counting detector computed tomography (PCD-CT) is a promising new technology with the potential to fundamentally change workflows in the daily routine and provide new quantitative imaging information to improve clinical decision-making and patient management. METHOD: The contents of this review are based on an unrestricted literature search of PubMed and Google Scholar using the search terms "photon-counting CT", "photon-counting detector", "spectral CT", "computed tomography" as well as on the authors' own experience. RESULTS: The fundamental difference with respect to the currently established energy-integrating CT detectors is that PCD-CT allows for the counting of every single photon at the detector level. Based on the identified literature, PCD-CT phantom measurements and initial clinical studies have demonstrated that the new technology allows for improved spatial resolution, reduced image noise, and new possibilities for advanced quantitative image postprocessing. CONCLUSION: For clinical practice, the potential benefits include fewer beam hardening artifacts, a radiation dose reduction, and the use of new or combinations of contrast agents. In particular, critical patient groups such as oncological, cardiovascular, lung, and head & neck as well as pediatric patient collectives benefit from the clinical advantages. KEY POINTS: · Photon-counting computed tomography (PCD-CT) is being used for the first time in routine clinical practice, enabling a significant dose reduction in critical patient populations such as oncology, cardiology, and pediatrics.. · Compared to conventional CT, PCD-CT enables a reduction in electronic image noise.. · Due to the spectral data sets, PCD-CT enables fully comprehensive post-processing applications.. CITATION FORMAT: · Hagen F, Soschynski M, Weis M et al. Photon-counting computed tomography - clinical application in oncological, cardiovascular, and pediatric radiology. Fortschr Röntgenstr 2024; 196: 25 - 34.

Diagnostic Accuracy of Contrast-Enhanced Thoracic Photon-Counting Computed Tomography for Opportunistic Locoregional Staging of Breast Cancer Compared With Digital Mammography: A Prospective Trial.

OBJECTIVE: Accurate locoregional staging is crucial for effective breast cancer treatment. Photon-counting computed tomography (PC-CT) is an emerging technology with high spatial resolution and the ability to depict uptake of contrast agents in tissues, making it a promising tool for breast cancer imaging. The aim of this study was to establish the feasibility of locoregional staging of breast cancer through contrast-enhanced thoracic PC-CT, assess its diagnostic performance, and compare it with that of digital mammography (DM). MATERIALS AND METHODS: Patients with newly diagnosed breast cancer, DM, and indication of thoracic CT staging were prospectively enrolled in this clinical cohort study over a period of 6 months. Participants underwent contrast-enhanced thoracic PC-CT and breast magnetic resonance imaging in prone position. After blinding to patient data, 2 radiologists independently rated PC-CT and DM regarding the following 6 characteristics: (1) diameter of the largest mass lesion, (2) infiltration of cutis/pectoral muscle/thoracic wall, (3) number of mass lesions, (4) presence/absence of adjacent ductal carcinoma in situ (DCIS), (5) tumor conspicuity, and (6) diagnostic confidence. Reference standard was generated from consensus reading of magnetic resonance imaging combined with all histopathological/clinical data by an independent adjudication committee applying TNM eighth edition. RESULTS: Among 32 enrolled female subjects (mean ± SD age, 59 ± 13.0 years), diagnostic accuracy for T-classification was higher for PC-CT compared with DM (0.94 vs 0.50, P < 0.01). Moreover, the correlation of the number of detected tumor masses with the reference standard was stronger for PC-CT than for DM (0.72 vs 0.50, P < 0.01). We observed that PC-CT significantly (P < 0.04) outperformed DM regarding not only sensitivity (0.83 and 0.25, respectively) but also specificity (0.99 and 0.80, respectively) for adjacent DCIS. The κ values for interreader reliability were higher for PC-CT compared with DM (mean 0.88 vs 0.54, respectively; P = 0.01). CONCLUSIONS: Photon-counting computed tomography outperformed DM in T-classification and provided higher diagnostic accuracy for the detection of adjacent DCIS. Therefore, opportunistic locoregional staging of breast cancer in contrast-enhanced thoracic PC-CT is feasible and could overcome limitations of DM with the potential to improve patient management.

Secondary dynamic midface reanimation with gracilis free muscle transfer after failed reconstruction attempt: A 15-year experience.

BACKGROUND: The quantitative outcome of secondary reanimation after a failed primary reconstruction attempt for facial paralysis is rarely reported in the literature. This study aimed to investigate the feasibility of secondary reanimation with gracilis free muscle transfer (GFMT) and whether this outcome is influenced by the primary reconstruction. METHODS: Twelve patients with previously failed static procedures (static group, n = 6), temporal muscle transfer (temporal transfer group, n = 2), and GFMT (GFMT group, n = 4) were all secondarily reanimated with GFMT. The clinical outcome was graded with the eFACE metric. The objective oral commissure excursion was measured with Emotrics, and the artificial intelligence software FaceReader evaluated the intensity score (IS) of emotional expression. RESULTS: The mean follow-up was 40 ± 27 months. The eFACE metric showed a statistically significant (p < 0.05) postoperative improvement in the dynamic and smile scores across all groups. In the GFMT group, oral commissure with smile (75.75 ± 20.43 points), oral commissure excursion while smiling with teeth showing (32.7 ± 4.35 mm), and the intensity of happiness emotion while smiling without teeth showing (IS of 0.37 ± 0.23) were significantly lower as compared with the static group postoperatively (98.83 ± 2.86 points, p = 0.038; 41.7 ± 4.35 mm, p = 0.025; IS 0.83 ± 0.16, p = 0.01). CONCLUSIONS: Our data suggest that secondary dynamic reconstruction with GFMT is feasible should the primary reconstruction fail. The secondary GFMT appears to improve the outcome of primary GFMT; however, the oral commissure excursion while smiling might be lower than that in patients who had static procedures as primary reconstruction.

Impact of different metal artifact reduction techniques in photon-counting computed tomography head and neck scans in patients with dental hardware.

OBJECTIVES: Metal artifacts remain a challenge in computed tomography. We investigated the potential of photon-counting computed tomography (PCD-CT) for metal artifact reduction using an iterative metal artifact reduction (iMAR) algorithm alone and in combination with high keV monoenergetic images (140 keV) in patients with dental hardware. MATERIAL AND METHODS: Consecutive patients with dental implants were prospectively included in this study and received PCD-CT imaging of the craniofacial area. Four series were reconstructed (standard [PCD-CTstd], monoenergetic at 140 keV [PCD-CT140keV], iMAR corrected [PCD-CTiMAR], combination of iMAR and 140 keV monoenergetic [PCD-CTiMAR+140keV]). All reconstructions were assessed qualitatively by four radiologists (independent and blinded reading on a 5-point Likert scale [5 = excellent; no artifact]) regarding overall image quality, artifact severity, and delineation of adjacent and distant anatomy. To assess signal homogeneity and evaluate the magnitude of artifact reduction, we performed quantitative measures of coefficient of variation (CV) and a region of interest (ROI)-based relative change in artifact reduction [PCD-CT/PCD-CTstd]. RESULTS: We enrolled 48 patients (mean age 66.5 ± 11.2 years, 50% (n = 24) males; mean BMI 25.2 ± 4.7 kg/m2; mean CTDIvol 6.2 ± 6 mGy). We found improved overall image quality, reduced artifacts and superior delineation of both adjacent and distant anatomy 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 = 0.42). The ROI-based analysis indicated the most effective artifact reduction for the iMAR reconstructions, which was significantly higher compared to PCD-CT140keV (p < 0.001). CONCLUSION: PCD-CT offers highly effective approaches for metal artifact reduction with the potential to overcome current diagnostic challenges in patients with dental implants. CLINICAL RELEVANCE STATEMENT: Metallic artifacts pose a significant challenge in CT imaging, potentially leading to missed findings. Our study shows that PCD-CT with iMAR post-processing reduces artifacts, improves image quality, and can possibly reveal pathologies previously obscured by artifacts, without additional dose application. KEY POINTS: • Photon-counting detector CT (PCD-CT) offers highly effective approaches for metal artifact reduction in patients with dental fillings/implants. • Iterative metal artifact reduction (iMAR) is superior to high keV monoenergetic reconstructions at 140 keV for artifact reduction and provides higher image quality. • Signal homogeneity of the reconstructed images is not affected by the different artifact reduction techniques.

Photon-counting computed tomography of coronary and peripheral artery stents: a phantom study.

Accurate small vessel stent visualization using CT remains challenging. Photon-counting CT (PCD-CT) may help to overcome this issue. We systematically investigate PCD-CT impact on small vessel stent assessment compared to energy-integrating-CT (EID). 12 water-contrast agent filled stents (3.0-8 mm) were scanned with patient-equivalent phantom using clinical PCD-CT and EID-CT. Images were reconstructed using dedicated vascular kernels. Subjective image quality was evaluated by 5 radiologists independently (5-point Likert-scale; 5 = excellent). Objective image quality was evaluated by calculating multi-row intensity profiles including edge rise slope (ERS) and coefficient-of-variation (CV). Highest overall reading scores were found for PCD-CT-Bv56 (3.6[3.3-4.3]). In pairwise comparison, differences were significant for PCD-CT-Bv56 vs. EID-CT-Bv40 (p ≤ 0.04), for sharpness and blooming respectively (all p < 0.05). Highest diagnostic confidence was found for PCD-CT-Bv56 (p ≤ 0.2). ANOVA revealed a significant effect of kernel strength on ERS (p < 0.001). CV decreased with stronger PCD-CT kernels, reaching its lowest in PCD-CT-Bv56 and highest in EID-CT reconstruction (p ≤ 0.05). We are the first study to verify, by phantom setup adapted to real patient settings, PCD-CT with a sharp vascular kernel provides the most favorable image quality for small vessel stent imaging. PCD-CT may reduce the number of invasive coronary angiograms, however, more studies needed to apply our results in clinical practice.

Impact of Photon-Counting Detector Computed Tomography on Image Quality and Radiation Dose in Patients With Multiple Myeloma.

OBJECTIVE: Computed tomography (CT) is an established method for the diagnosis, staging, and treatment of multiple myeloma. Here, we investigated the potential of photon-counting detector computed tomography (PCD-CT) in terms of image quality, diagnostic confidence, and radiation dose compared with energy-integrating detector CT (EID-CT). MATERIALS AND METHODS: In this prospective study, patients with known multiple myeloma underwent clinically indicated whole-body PCD-CT. The image quality of PCD-CT was assessed qualitatively by three independent radiologists for overall image quality, edge sharpness, image noise, lesion conspicuity, and diagnostic confidence using a 5-point Likert scale (5 = excellent), and quantitatively for signal homogeneity using the coefficient of variation (CV) of Hounsfield Units (HU) values and modulation transfer function (MTF) via the full width at half maximum (FWHM) in the frequency space. The results were compared with those of the current clinical standard EID-CT protocols as controls. Additionally, the radiation dose (CTDIvol) was determined. RESULTS: We enrolled 35 patients with multiple myeloma (mean age 69.8 ± 9.1 years; 18 [51%] males). Qualitative image analysis revealed superior scores (median [interquartile range]) for PCD-CT regarding overall image quality (4.0 [4.0-5.0] vs. 4.0 [3.0-4.0]), edge sharpness (4.0 [4.0-5.0] vs. 4.0 [3.0-4.0]), image noise (4.0 [4.0-4.0] vs. 3.0 [3.0-4.0]), lesion conspicuity (4.0 [4.0-5.0] vs. 4.0 [3.0-4.0]), and diagnostic confidence (4.0 [4.0-5.0] vs. 4.0 [3.0-4.0]) compared with EID-CT (P ≤ 0.004). In quantitative image analyses, PCD-CT compared with EID-CT revealed a substantially lower FWHM (2.89 vs. 25.68 cy/pixel) and a significantly more homogeneous signal (mean CV ± standard deviation [SD], 0.99 ± 0.65 vs. 1.66 ± 0.5; P < 0.001) at a significantly lower radiation dose (mean CTDIvol ± SD, 3.33 ± 0.82 vs. 7.19 ± 3.57 mGy; P < 0.001). CONCLUSION: Whole-body PCD-CT provides significantly higher subjective and objective image quality at significantly reduced radiation doses than the current clinical standard EID-CT protocols, along with readily available multi-spectral data, facilitating the potential for further advanced post-processing.

Diagnostic Accuracy of Indocyanine Green Clearance Test for Different Stages of Liver Fibrosis and Cirrhosis.

(1) Background: This study aimed to correlate the indocyanine green clearance (ICG) test with histopathological grades of liver fibrosis and liver cirrhosis to assess its diagnostic accuracy in differentiating normal liver parenchyma from liver fibrosis and liver cirrhosis. (2) Methods: A total of 82 patients who received a histopathological liver examination, imaging, and ICG test within three months were included in this retrospective study. The histopathological level of fibrosis was graded using the Ishak scoring system, and the patients were divided into five categories: no liver fibrosis (NLF), mild liver fibrosis (MLF), advanced liver fibrosis (ALF), severe liver fibrosis (SLF), and liver cirrhosis (LC). The non-parametric Kruskal-Wallis test with post hoc pairwise comparison utilizing Mann-Whitney U tests and Bonferroni adjustment was used to analyze differences in the ICG test results between the patient groups. Cross correlation between the individual fibrosis/cirrhosis stages and the score of the ICG test was performed, and the sensitivity, specificity, and positive and negative predictive values were calculated for each model predicting liver fibrosis/cirrhosis. (3) Results: A significant difference (p ≤ 0.001) between stages of NLF, LF, and LC was found for the ICG parameters (ICG plasma disappearance rate (ICG-PDR) and ICG retention percentage at 15 min (ICG-R15)). The post hoc analysis revealed that NLF significantly differed from SLF (ICG-PDR: p = 0.001; ICG-R15: p = 0.001) and LC (ICG-PDR: p = 0.001; ICG-R15: p = 0.001). ALF also significantly differed from SLF (ICG-PDR: p = 0.033; ICG-R15: p = 0.034) and LC (ICG-PDR: p = 0.014; ICG-R15: p = 0.014). The sensitivity for detection of an initial stage of liver fibrosis compared to no liver fibrosis (Ishak ≥ 1) was 0.40; the corresponding specificity was 0.80. The differentiation of advanced liver fibrosis or cirrhosis (Ishak ≥ 4) compared to other stages of liver fibrosis was 0.75, with a specificity of 0.81. (4) Conclusions: This study shows that the ICG test, as a non-invasive diagnostic test, is able to differentiate patients with no liver fibrosis from patients with advanced liver fibrosis and liver cirrhosis. The ICG test seems to be helpful in monitoring patients with liver fibrosis regarding compensation levels, thus potentially enabling physicians to both detect progression from compensated liver fibrosis to advanced liver fibrosis and cirrhosis and to initiate antifibrotic treatment at an earlier stage.

Age-related outcomes of facial reanimation surgery using gracilis free functional muscle transfer innervated by the masseteric nerve: A retrospective cohort study.

BACKGROUND: The free functional muscle gracilis transfer is an established approach in facial reanimation surgery; however, the significance of its neurotization and the patient's age is still inconclusive. Several donor nerves are available for facial reanimation using the free functional gracilis muscle transfer. OBJECTIVE: This retrospective cohort study investigates whether the masseteric nerve is an equally reliable donor nerve in both older and younger patients. METHODS: We included 46 patients (13-71 years, male and female) who underwent nerve-to-masseter (NTM)-driven free functional muscle transfer (FFMT) between January 2008 and December 2019. Patients were distributed into three cohorts according to their age at surgery. We assessed the facial symmetry before and after surgery using the pupillo-modiolar angle. Commissure height and excursion deviation were measured with the Emotrics software. Patient-reported outcome measurements were taken using the Facial Clinimetric Examination (FaCE) scale. RESULTS: All patients had successful flap innervation, except for one patient in the middle-aged cohort (31-51 years). The postoperative facial symmetry at rest, smiling, and laughing was analyzed with the pupillo-modiolar angle and the Emotrics software and showed similar results between all cohorts. The FaCE scale showed similar scores for the middle-aged (31-51 years) cohort and the senior cohort (52-71 years). The social function score in the senior cohort was higher than in the middle-aged cohort, without statistical significance. One patient in the middle-aged (31-51 years) cohort and the senior cohort (52-71 years), respectively, underwent emergency revision due to impaired flap perfusion and could be salvaged. CONCLUSIONS: NTM-driven FFMT for facial reanimation is a safe and reliable procedure across all age groups of patients.

Predictive Value of [99mTc]-MAA-Based Dosimetry in Hepatocellular Carcinoma Patients Treated with [90Y]-TARE: A Single-Center Experience.

Transarterial radioembolization is a well-established method for the treatment of hepatocellular carcinoma. The tolerability and incidence of hepatic decompensation are related to the doses delivered to the tumor and healthy liver. This retrospective study was performed at our center to evaluate whether tumor- and healthy-liver-absorbed dose levels in TARE are predictive of tumor response according to the mRECIST 1.1 criteria and overall survival. One hundred and six patients with hepatocellular carcinoma were treated with [90Y]-loaded resin microspheres and completed the follow-up. The dose delivered to each compartment was calculated using a compartmental model. The model was based on [99mTc]-labelled albumin aggregate images obtained before the start of therapy. Tumor response was assessed after three months of treatment. Kaplan-Meier analysis was used to assess survival. The mean age of our population was 66 ± 13 years with a majority being BCLC B tumors. Forty-two patients presented with portal vein thrombosis. The response rate was 57% in the overall population and 59% in patients with thrombosis. Target-to-background (TBR) values measured on initial [99mTc]MAA-SPECT-imaging and tumor model dosimetric values were associated with tumor response (p < 0.001 and p = 0.009, respectively). A dosimetric threshold of 136.5 Gy was predictive of tumor response with a sensitivity of 84.2% and specificity of 89.4%. Overall survival was 24.1 months [IQR 13.1-36.4] for patients who responded to treatment compared to 10.4 months [IQR 6.3-15.9] for the remaining patients (p = 0.022). In this cohort, the initial [99mTc]MAA imaging is predictive of response and survival. The dosimetry prior to the application of TARE can be used for treatment planning and our results also suggest that the therapy is well-tolerated. In particular, hepatic decompensation can be predicted even in the presence of PVT.

Accelerated Diffusion-Weighted Imaging in 3 T Breast MRI Using a Deep Learning Reconstruction Algorithm With Superresolution Processing: A Prospective Comparative Study.

OBJECTIVES: Diffusion-weighted imaging (DWI) enhances specificity in multiparametric breast MRI but is associated with longer acquisition time. Deep learning (DL) reconstruction may significantly shorten acquisition time and improve spatial resolution. In this prospective study, we evaluated acquisition time and image quality of a DL-accelerated DWI sequence with superresolution processing (DWI DL ) in comparison to standard imaging including analysis of lesion conspicuity and contrast of invasive breast cancers (IBCs), benign lesions (BEs), and cysts. MATERIALS AND METHODS: This institutional review board-approved prospective monocentric study enrolled participants who underwent 3 T breast MRI between August and December 2022. Standard DWI (DWI STD ; single-shot echo-planar DWI combined with reduced field-of-view excitation; b-values: 50 and 800 s/mm 2 ) was followed by DWI DL with similar acquisition parameters and reduced averages. Quantitative image quality was analyzed for region of interest-based signal-to-noise ratio (SNR) on breast tissue. Apparent diffusion coefficient (ADC), SNR, contrast-to-noise ratio, and contrast (C) values were calculated for biopsy-proven IBCs, BEs, and for cysts. Two radiologists independently assessed image quality, artifacts, and lesion conspicuity in a blinded independent manner. Univariate analysis was performed to test differences and interrater reliability. RESULTS: Among 65 participants (54 ± 13 years, 64 women) enrolled in the study, the prevalence of breast cancer was 23%. Average acquisition time was 5:02 minutes for DWI STD and 2:44 minutes for DWI DL ( P < 0.001). Signal-to-noise ratio measured in breast tissue was higher for DWI STD ( P < 0.001). The mean ADC values for IBC were 0.77 × 10 -3 ± 0.13 mm 2 /s in DWI STD and 0.75 × 10 -3 ± 0.12 mm 2 /s in DWI DL without significant difference when sequences were compared ( P = 0.32). Benign lesions presented with mean ADC values of 1.32 × 10 -3 ± 0.48 mm 2 /s in DWI STD and 1.39 × 10 -3 ± 0.54 mm 2 /s in DWI DL ( P = 0.12), and cysts presented with 2.18 × 10 -3 ± 0.49 mm 2 /s in DWI STD and 2.31 × 10 -3 ± 0.43 mm 2 /s in DWI DL . All lesions presented with significantly higher contrast in the DWI DL ( P < 0.001), whereas SNR and contrast-to-noise ratio did not differ significantly between DWI STD and DWI DL regardless of lesion type. Both sequences demonstrated a high subjective image quality (29/65 for DWI STD vs 20/65 for DWI DL ; P < 0.001). The highest lesion conspicuity score was observed more often for DWI DL ( P < 0.001) for all lesion types. Artifacts were scored higher for DWI DL ( P < 0.001). In general, no additional artifacts were noted in DWI DL . Interrater reliability was substantial to excellent (k = 0.68 to 1.0). CONCLUSIONS: DWI DL in breast MRI significantly reduced scan time by nearly one half while improving lesion conspicuity and maintaining overall image quality in a prospective clinical cohort.

The Cannabis Effects Expectancy Questionnaire-Medical (CEEQ-M): Preliminary psychometric properties and longitudinal validation within a clinical trial.

The use of cannabis for medical symptoms is increasing despite limited evidence for its efficacy. Expectancies-prior beliefs about a substance or medicine-can modulate use patterns and effects of medicines on target symptoms. To our knowledge, cannabis expectancies have not been studied for their predictive value for symptom relief. The 21-item Cannabis Effects Expectancy Questionnaire-Medical (CEEQ-M) is the first longitudinally validated measure of expectancies for cannabis used for medical symptoms. The questionnaire was developed for a randomized clinical trial of the effect of state cannabis registration (SCR) card ownership on symptoms of pain, insomnia, anxiety, and depression in adults (N = 269 across six questionnaire administrations). Item-level analyses (n = 188) demonstrated between-person stability of expectancies and no aggregate, within-person expectancy changes 3 months after individuals gained access to SCR cards. Exploratory factor analysis (n = 269) indicated a two-factor structure. Confirmatory factor analysis at a later timepoint (n = 193) demonstrated good fit and scalar invariance of the measurement model. Cross-lagged panel models across 3 and 12 months (n = 187 and 161, respectively) indicated that CEEQ-M-measured expectancies did not predict changes in self-reported cannabis use; symptoms of pain, insomnia, anxiety, and depression; and well-being. However, greater baseline cannabis use predicted more positive expectancy changes. The findings suggest that the CEEQ-M is psychometrically sound. Future work should clarify at what timescales cannabis expectancies have predictive value and how cannabis expectancies for medical symptoms are maintained and diverge from other substance use expectancies. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Accuracy of Ultrahigh-Resolution Photon-counting CT for Detecting Coronary Artery Disease in a High-Risk Population.

Background Recently introduced photon-counting CT may improve noninvasive assessment of patients with high risk for coronary artery disease (CAD). Purpose To determine the diagnostic accuracy of ultrahigh-resolution (UHR) coronary CT angiography (CCTA) in the detection of CAD compared with the reference standard of invasive coronary angiography (ICA). Materials and Methods In this prospective study, participants with severe aortic valve stenosis and clinically indicated CT for transcatheter aortic valve replacement planning were consecutively enrolled from August 2022 to February 2023. All participants were examined with a dual-source photon-counting CT scanner using a retrospective electrocardiography-gated contrast-enhanced UHR scanning protocol (tube voltage, 120 or 140 kV; collimation, 120 × 0.2 mm; 100 mL of iopromid; no spectral information). Subjects underwent ICA as part of their clinical routine. A consensus assessment of image quality (five-point Likert scale: 1 = excellent [absence of artifacts], 5 = nondiagnostic [severe artifacts]) and a blinded independent reading for the presence of CAD (stenosis ≥50%) were performed. UHR CCTA was compared with ICA using area under the receiver operating characteristic curve (AUC). Results Among 68 participants (mean age, 81 years ± 7 [SD]; 32 male, 36 female), the prevalence of CAD and prior stent placement was 35% and 22%, respectively. The overall image quality was excellent (median score, 1.5 [IQR, 1.3-2.0]). The AUC of UHR CCTA in the detection of CAD was 0.93 per participant (95% CI: 0.86, 0.99), 0.94 per vessel (95% CI: 0.91, 0.98), and 0.92 per segment (95% CI: 0.87, 0.97). Sensitivity, specificity, and accuracy, respectively, were 96%, 84%, and 88% per participant (n = 68); 89%, 91%, and 91% per vessel (n = 204); and 77%, 95%, and 95% per segment (n = 965). Conclusion UHR photon-counting CCTA provided high diagnostic accuracy in the detection of CAD in a high-risk population, including subjects with severe coronary calcification or prior stent placement. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Williams and Newby in this issue.