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Thi Phuong Thao Hoang,1 Philipp Schindler,2 Nikolaus Börner,3 Max Masthoff,2 Mirjam Gerwing,2 Philippa von Beauvais,2 Enrico N De Toni,4 Christian M Lange,4 Jonel Trebicka,5 Haluk Morgül,6 Max Seidensticker,1 Jens Ricke,1 Andreas Pascher,6 Markus Guba,3 Michael Ingrisch,1 Moritz Wildgruber,1,* Osman Öcal1,* 1Department of Radiology, University Hospital, LMU Munich, Munich, Germany; 2Clinic for Radiology, University Hospital Muenster, Muenster, Germany; 3Department of General, Visceral and Transplantation Surgery, University Hospital, LMU Munich, Munich, Germany; 4Department for Internal Medicine II, University Hospital, LMU Munich, Munich, Germany; 5Department for Internal Medicine B, Universitätsklinikum Münster, Münster, Germany; 6Department of General, Visceral and Transplant Surgery, Universitätsklinikum Münster, Münster, Germany*These authors contributed equally to this workCorrespondence: Osman Öcal, Department of Radiology, University Hospital – LMU Munich, Marchioninistrasse 15, D-81377, München, Germany, Email osman.oecal@med.uni-muenchen.dePurpose: To investigate the prognostic value of computed tomography (CT) derived imaging biomarkers in hepatocellular carcinoma (HCC) recurrence after liver transplantation (LT) and develop a predictive nomogram model.Patients and Methods: This retrospective study included 178 patients with histopathologically confirmed HCC who underwent liver transplantation between 2007 and 2021 at the two academic liver centers. We evaluated dedicated imaging features from baseline multiphase contrast-enhanced CT supplemented by several clinical findings and laboratory parameters. Time-to-recurrence was estimated by Kaplan–Meier analysis. Univariable Cox proportional hazard regression and multivariable Least Absolute Shrinkage and Selection Operator (LASSO) regression were used to assess independent prognostic factors for recurrence. A nomogram model was then built based on the independent factors selected through LASSO regression, to predict the probabilities of HCC recurrence at one, three, and five years.Results: The rate of HCC recurrence after LT was 17.4% (31 of 178). The LASSO analysis revealed six independent predictors associated with an elevated risk of tumor recurrence. These predictors included the presence of peritumoral enhancement, the presence of over three tumor lesions, the largest tumor diameter greater than 3 cm, serum alpha-fetoprotein (AFP) levels exceeding 400 ng/mL, and the presence of a tumor capsule. Conversely, a history of bridging therapies was found to be correlated with a reduced risk of HCC recurrence. In addition, Kaplan-Meier curves showed patients with irregular margin, satellite nodules, or small lesions displayed shorter time-to-recurrence. Our nomogram demonstrated good performance, yielding a C-index of 0.835 and AUC values of 0.86, 0.88, and 0.85 for the predictions of 1-year, 3-year, and 5-year TTR, respectively.Conclusion: Imaging parameters derived from baseline contrast-enhanced CT showing malignant behavior and aggressive growth patterns, along with serum AFP and history of bridging therapies, show potential as biomarkers for predicting HCC recurrence after transplantation.Keywords: hepatocellular carcinoma, transplantation, imaging, recurrence

Objective: Cerebral small vessel diseases (SVDs) are associated with cerebrovascular dysfunction, such as increased blood-brain barrier leakage (permeability surface area product), vascular pulsatility, and decreased cerebrovascular reactivity (CVR). No studies assessed all 3 functions concurrently. We assessed 3 key vascular functions in sporadic and genetic SVD to determine associations with SVD severity, subtype, and interrelations., Methods: In this prospective, cross-sectional, multicenter INVESTIGATE-SVDs study, we acquired brain magnetic resonance imaging in patients with sporadic SVD/cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL), including structural, quantitative microstructural, permeability surface area product, blood plasma volume fraction, vascular pulsatility, and CVR (in response to CO 2 ) scans. We determined vascular function and white matter hyperintensity (WMH) associations, using covariate-adjusted linear regression; normal-appearing white matter and WMH differences, interrelationships between vascular functions, using linear mixed models; and major sources of variance using principal component analyses., Results: We recruited 77 patients (45 sporadic/32 CADASIL) at 3 sites. In adjusted analyses, patients with worse WMH had lower CVR (B = -1.78, 95% CI -3.30, -0.27) and blood plasma volume fraction (B = -0.594, 95% CI -0.987, -0.202). CVR was worse in WMH than normal-appearing white matter (eg, CVR: B = -0.048, 95% CI -0.079, -0.017). Adjusting for WMH severity, SVD subtype had minimal influence on vascular function (eg, CVR in CADASIL vs sporadic: B = 0.0169, 95% CI -0.0247, 0.0584). Different vascular function mechanisms were not generally interrelated (eg, permeability surface area product~CVR: B = -0.85, 95% CI -4.72, 3.02). Principal component analyses identified WMH volume/quantitative microstructural metrics explained most variance in CADASIL and arterial pulsatility in sporadic SVD, but similar main variance sources., Interpretation: Vascular function was worse with higher WMH, and in WMH than normal-appearing white matter. Sporadic SVD-CADASIL differences largely reflect disease severity. Limited vascular function interrelations may suggest disease stage-specific differences. ANN NEUROL 2024., (© 2024 The Author(s). Annals of Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association.)

Purpose: Newly onset conduction disturbances with the need for permanent pacemaker (PPM) implantation remain the most common complication of transcatheter aortic valve replacement (TAVR). The objective was to evaluate the predictive value of clinical, ECG and new pre-procedural CT-imaging parameters for the requirement of PPM-implantation after TAVR., Methods: 2105 consecutive patients receiving TAVR using a balloon expandable prosthesis (Sapien 3, Edwards Lifesciences, Irving, CA, USA) at our institution were enrolled. Patients receiving a valve-in-valve prosthesis, TAVR after surgical repair, with missing or non-diagnostic CT-scans, with pre-implanted PPM and after TAVR in mitral position were excluded. The most suitable classification model for the given dataset was first identified through benchmark testing and later applied for prediction analysis., Results: 312 eligible patients requiring PPM implantation were compared to an age- matched control group of 305 patients not requiring PPM implantation. A scaled LASSO model allowed for most accurate prediction with an AUC of 0.70. Right bundle branch block was the strongest predictor (OR 2.739), followed by atrioventricular block 1° (OR 2.091), prosthesis diameter (OR 1.351), atrial fibrillation (OR 1.255), arterial hypertension (OR 1.215), coronary artery disease (1.070), the angle of ventricle axis and aortic root (OR 1.030), sinotubular junction height (OR 1.014) and the calcification of the left coronary cuspid (OR 1.007)., Conclusions: ECG- and clinical outperform imaging parameters in predicting PPM-implantation following TAVR. Right bundle branch block emerged as the most significant predictor overall, while the angle of ventricle axis and aortic root as a novel imaging-based predictor., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Objectives: In this multi-center study, we proposed a structured reporting (SR) framework for non-small cell lung cancer (NSCLC) and developed a software-assisted tool to automatically translate image-based findings and annotations into TNM classifications. The aim of this study was to validate the software-assisted SR tool for NSCLC, assess its potential clinical impact in a proof-of-concept study, and evaluate current reporting standards in participating institutions., Methods: A framework for SR and staging of NSCLC was developed in a multi-center collaboration. SR annotations and descriptions were used to generate semi-automated TNM classification. The SR and TNM classification tools were evaluated by nine radiologists on n = 20 representative [18F]FDG PET/CT studies and compared to the free text reporting (FTR) strategy. Results were compared to a multidisciplinary team reference using a generalized linear mixed model (GLMM). Additionally, participants were surveyed on their experience with SR and TNM classification., Results: Overall, GLMM analysis revealed that readers using SR were 1.707 (CI: 1.137-2.585) times more likely to correctly classify TNM status compared to FTR strategy (p = 0.01) resulting in increased overall TNM correctness in 71.9% (128/178) of cases compared to 62.8% (113/180) FTR. The primary source of variation in classification accuracy was explained by case complexity. Participants rated the potential impact of SR and semi-automated TNM classification as positive across all categories with improved scores after template validation., Conclusion: This multi-center study yielded an effective software-assisted SR framework for NSCLC. The SR and semi-automated classification tool improved TNM classification and were perceived as valuable., Critical Relevance Statement: Software-assisted SR provides robust input for semi-automated rule-based TNM classification in non-small-cell lung carcinoma (NSCLC), improves TNM correctness compared to FTR, and was perceived as valuable by radiology physicians., Key Points: SR and TNM classification are underutilized across participating centers for NSCLC staging. Software-assisted SR has emerged as a promising strategy for oncologic assessment. Software-assisted SR facilitates semi-automated TNM classification with improved staging accuracy compared to free-text reports in NSCLC., (© 2024. The Author(s).)