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PURPOSE: Alteration in mitochondrial membrane potential (ΔΨ(m)) is an important feature of many pathologic processes, including heart failure, cardiotoxicity, ventricular arrhythmia, and myocardial hypertrophy. We present the first in vivo, noninvasive, assessment of regional ΔΨ(m) in the myocardium of normal human subjects. METHODS: Thirteen healthy subjects were imaged using [(18)F]-triphenylphosphonium ([(18)F]TPP+) on a PET/MR scanner. The imaging protocol consisted of a bolus injection of 300 MBq followed by a 120 min infusion of 0.6 MBq/min. A 60 min dynamic PET acquisition was started 1 hour after bolus injection. The extracellular space fraction (f(ECS)) was simultaneously measured using MR T1-mapping images acquired at baseline and 15 minutes after gadolinium injection with correction for the subject’s hematocrit level. Serial venous blood samples were obtained to calculate the plasma tracer concentration. The tissue membrane potential (ΔΨ(T)), a proxy of ΔΨ(m), was calculated from the myocardial tracer concentration at secular equilibrium, blood concentration, and fECS measurements using a model based on the Nernst equation. RESULTS: In 13 healthy subjects, average tissue membrane potential (ΔΨ(T)), representing the sum of cellular membrane potential (ΔΨ(c)) and ΔΨ(m), was −160.7±3.7 mV, in excellent agreement with previous in vitro assessment. CONCLUSION: In vivo quantification of the mitochondrial function has the potential to provide new diagnostic and prognostic information for several cardiac diseases as well as allowing therapy monitoring. This feasibility study lays the foundation for further investigations to assess these potential roles. ClinicalTrial Identifier: NCT03265431

Categorical regressor variables are usually handled by introducing a set of indicator variables, and imposing a linear constraint to ensure identifiability in the presence of an intercept, or equivalently, using one of various coding schemes. As proposed in Yuan and Lin (2006), the group lasso is a natural and computationally convenient approach to perform variable selection in settings with categorical covariates. As pointed out by Simon and Tibshirani (2012), “standardization” by means of block-wise orthonormalization of column submatrices each corresponding to one group of variables can substantially boost performance. In this note, we study the aspect of standardization for the special case of categorical predictors in detail. The main result is that orthonormalization is not required; column-wise scaling of the design matrix followed by re-scaling and centering of the coefficients is shown to have exactly the same effect. Similar reductions can be achieved in the case of interactions. The extension to the so-called sparse group lasso, which additionally promotes within-group sparsity, is considered as well. The importance of proper standardization is illustrated via simulations and a case study.

PURPOSE OF REVIEW: To present a method enabling in-vivo quantification of tissue membrane potential (ΔΨ(T)), a proxy of mitochondrial membrane potential (ΔΨ(m)), to review the origin and role of ΔΨ(m), and highlight potential applications of myocardial ΔΨ(T) imaging. RECENT FINDINGS: Radiolabelled lipophilic cations have been used for decades to measure ΔΨ(m) in-vitro. Using similar compounds labeled with positron emitters and appropriate compartment modelling, this technique now allows in vivo quantification of ΔΨ(T) with positron emission tomography. Studies have confirmed the feasibility of measuring myocardial ΔΨ(T) in both animals and humans. In addition, ΔΨ(T) showed very low variability among healthy subjects, suggesting that this method could allow detection of relatively small pathological changes. SUMMARY: In-vivo assessment of myocardial ΔΨ(T) provides a new tool to study the pathophysiology of cardiovascular diseases and has the potential to serve as a new biomarker to assess disease stage, prognosis, and response to therapy.

BackgroundBlebs are rupture risk factors in intracranial aneurysms (IAs), but their prevalence, distribution, and associations with clinical factors as well as their causes and effects on aneurysm vulnerability remain unclear.MethodsA total of 122 blebs in 270 IAs selected for surgery were studied using patient-specific vascular reconstructions from 3D angiographic images. Bleb geometry, location on the aneurysm, and frequency of occurrence in aneurysms at different locations were analyzed. Associations between gender, age, smoking, hypertension, hormone therapy, dental infection, and presence of blebs were investigated.ResultsOf all aneurysms with blebs, 77% had a single bleb and 23% had multiple blebs. Only 6% of blebs were at the neck, while 46% were in the body and 48% in the dome. Aneurysms with blebs were larger (pConclusionsBlebs are common in IAs, and most aneurysms harboring blebs have a single bleb. Blebs in the aneurysm neck are rare, but they are equally common in the body and dome. The presence of blebs in IAs was associated with dental infection, and negatively associated with hormone replacement therapy.

Erworben im Rahmen der Schweizer Nationallizenzen (http://www.nationallizenzen.ch) Purpose: Incidental aneurysms pose a challenge to physicians who need to decide whether or not to treat them. A statistical model could potentially support such treatment decisions. The aim of this study was to compare a previously developed aneurysm rupture logistic regression probability model (LRM) to other machine learning (ML) classifiers for discrimination of aneurysm rupture status. Methods: Hemodynamic, morphological, and patient-related information of 1631 cerebral aneurysms characterized by computational fluid dynamics simulations were used to train support vector machines (SVMs) with linear and RBF kernel (RBF-SVM), k-nearest neighbors (kNN), decision tree, random forest, and multilayer perceptron (MLP) neural network classifiers for predicting the aneurysm rupture status. The classifiers’ accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were evaluated and compared to the LRM using 249 test cases obtained from two external cohorts. Additionally, important variables were determined based on the random forest and weights of the linear SVM. Results: The AUCs of the MLP, LRM, linear SVM, RBF-SVM, kNN, decision tree, and random forest were 0.83, 0.82, 0.80, 0.81, 0.76, 0.70, and 0.79, respectively. The accuracy ranged between 0.76 (decision tree,) and 0.79 (linear SVM, RBF-SVM, and MLP). Important variables for predicting the aneurysm rupture status included aneurysm location, the mean surface curvature, and maximum flow velocity. Conclusion: The performance of the LRM was overall comparable to that of the other ML classifiers, confirming its potential for aneurysm rupture assessment. To further improve the predictions, additional information, e.g., related to the aneurysm wall, might be needed.

Erworben im Rahmen der Schweizer Nationallizenzen (http://www.nationallizenzen.ch) Background: Hemodynamic patterns have been associated with cerebral aneurysm instability. For patient-specific computational fluid dynamics (CFD) simulations, the inflow rates of a patient are typically not known. The aim of this study was to analyze the influence of inter- and intra-patient variations of cerebral blood flow on the computed hemodynamics through CFD simulations and to incorporate these variations into statistical models for aneurysm rupture prediction. Methods: Image data of 1820 aneurysms were used for patient-specific steady CFD simulations with nine different inflow rates per case, capturing inter- and intra-patient flow variations. Based on the computed flow fields, 17 hemodynamic parameters were calculated and compared for the different flow conditions. Next, statistical models for aneurysm rupture were trained in 1571 of the aneurysms including hemodynamic parameters capturing the flow variations either by defining hemodynamic “response variables” (model A) or repeatedly randomly selecting flow conditions by patients (model B) as well as morphological and patient-specific variables. Both models were evaluated in the remaining 249 cases. Results: All hemodynamic parameters were significantly different for the varying flow conditions (p

BACKGROUND AND PURPOSE: Intracranial aneurysms originating at the posterior communicating artery are known to have high rupture risk compared with other locations. We tested the hypothesis that different angioarchitectures (ie, branch point configuration) of posterior communicating artery aneurysms are associated with aneurysm hemodynamics, which in turn predisposes aneurysms to rupture. MATERIALS AND METHODS: A total of 313 posterior communicating artery aneurysms (145 ruptured, 168 unruptured) were studied with image-based computational fluid dynamics. Aneurysms were classified into different angioarchitecture types depending on the location of the aneurysm with respect to parent artery bifurcation. Hemodynamic characteristics were compared between ruptured and unruptured aneurysms, as well as among aneurysms with different angioarchitectures. RESULTS: Angioarchitecture was associated with rupture ( P = .003). Ruptured aneurysms had higher, more concentrated, and more oscillatory wall shear stress distributions (maximum wall shear stress, P P P P = .01; inflow concentration index, P P P P = .02) and minimum wall shear stress ( P = .002) than unruptured aneurysms. CONCLUSIONS: High-flow intrasaccular hemodynamic characteristics, commonly found in bifurcation-type angioarchitectures, are associated with the posterior communicating artery aneurysm rupture status. These characteristics include strong and concentrated inflow jets, concentrated regions of elevated wall shear stress, oscillatory wall shear stress, lower normalized wall shear stress, and complex and unstable flow patterns.

OBJECTIVEIncidental aneurysms pose a challenge for physicians, who need to weigh the rupture risk against the risks associated with treatment and its complications. A statistical model could potentially support such treatment decisions. A recently developed aneurysm rupture probability model performed well in the US data used for model training and in data from two European cohorts for external validation. Because Japanese and Finnish patients are known to have a higher aneurysm rupture risk, the authors’ goals in the present study were to evaluate this model using data from Japanese and Finnish patients and to compare it with new models trained with Finnish and Japanese data.METHODSPatient and image data on 2129 aneurysms in 1472 patients were used. Of these aneurysm cases, 1631 had been collected mainly from US hospitals, 249 from European (other than Finnish) hospitals, 147 from Japanese hospitals, and 102 from Finnish hospitals. Computational fluid dynamics simulations and shape analyses were conducted to quantitatively characterize each aneurysm’s shape and hemodynamics. Next, the previously developed model’s discrimination was evaluated using the Finnish and Japanese data in terms of the area under the receiver operating characteristic curve (AUC). Models with and without interaction terms between patient population and aneurysm characteristics were trained and evaluated including data from all four cohorts obtained by repeatedly randomly splitting the data into training and test data.RESULTSThe US model’s AUC was reduced to 0.70 and 0.72, respectively, in the Finnish and Japanese data compared to 0.82 and 0.86 in the European and US data. When training the model with Japanese and Finnish data, the average AUC increased only slightly for the Finnish sample (to 0.76 ± 0.16) and Finnish and Japanese cases combined (from 0.74 to 0.75 ± 0.14) and decreased for the Japanese data (to 0.66 ± 0.33). In models including interaction terms, the AUC in the Finnish and Japanese data combined increased significantly to 0.83 ± 0.10.CONCLUSIONSDeveloping an aneurysm rupture prediction model that applies to Japanese and Finnish aneurysms requires including data from these two cohorts for model training, as well as interaction terms between patient population and the other variables in the model. When including this information, the performance of such a model with Japanese and Finnish data is close to its performance with US or European data. These results suggest that population-specific differences determine how hemodynamics and shape associate with rupture risk in intracranial aneurysms.

Background: For a treatment decision of unruptured cerebral aneurysms, physicians and patients need to weigh the risk of treatment against the risk of hemorrhagic stroke caused by aneurysm rupture. The aim of this study was to externally evaluate a recently developed statistical aneurysm rupture probability model, which could potentially support such treatment decisions. Methods: Segmented image data and patient information obtained from two patient cohorts including 203 patients with 249 aneurysms were used for patient-specific computational fluid dynamics simulations and subsequent evaluation of the statistical model in terms of accuracy, discrimination, and goodness of fit. The model’s performance was further compared to a similarity-based approach for rupture assessment by identifying aneurysms in the training cohort that were similar in terms of hemodynamics and shape compared to a given aneurysm from the external cohorts. Results: When applied to the external data, the model achieved a good discrimination and goodness of fit (area under the receiver operating characteristic curve AUC = 0.82), which was only slightly reduced compared to the optimism-corrected AUC in the training population (AUC = 0.84). The accuracy metrics indicated a small decrease in accuracy compared to the training data (misclassification error of 0.24 vs. 0.21). The model’s prediction accuracy was improved when combined with the similarity approach (misclassification error of 0.14). Conclusions: The model’s performance measures indicated a good generalizability for data acquired at different clinical institutions. Combining the model-based and similarity-based approach could further improve the assessment and interpretation of new cases, demonstrating its potential use for clinical risk assessment.

PURPOSE: Unruptured cerebral aneurysms pose a dilemma for physicians who need to weigh the risk of a devastating subarachnoid hemorrhage against the risk of surgery or endovascular treatment and their complications when deciding on a treatment strategy. A prediction model could potentially support such treatment decisions. The aim of this study was to develop and internally validate a model for aneurysm rupture based on hemodynamic and geometric parameters, aneurysm location, and patient gender and age. METHODS: Cross-sectional data from 1,061 patients were used for image-based computational fluid dynamics and shape characterization of 1,631 aneurysms for training an aneurysm rupture probability model using logistic group Lasso regression. The model’s discrimination and calibration were internally validated based on the area under the curve (AUC) of the receiver operating characteristic (ROC) and calibration plots. RESULTS: The final model retained 11 hemodynamic and 12 morphological variables, aneurysm location, as well as patient age and gender. An adverse hemodynamic environment characterized by a higher maximum oscillatory shear index, higher kinetic energy and smaller low shear area as well as a more complex aneurysm shape, male gender and younger age were associated with an increased rupture risk. The corresponding AUC of the model was 0.86 (95% CI [0.85, 0.86], after correction for optimism 0.84). CONCLUSION: The model combining variables from various domains was able to discriminate between ruptured and unruptured aneurysms with an AUC of 86%. Internal validation indicated potential for the application of this model in clinical practice after evaluation with longitudinal data.

BACKGROUND: Intracranial aneurysms at the posterior communicating artery (PCOM) are known to have high rupture rates compared to other locations. We developed and internally validated a statistical model discriminating between ruptured and unruptured PCOM aneurysms based on hemodynamic and geometric parameters, angio-architectures, and patient age with the objective of its future use for aneurysm risk assessment. METHODS: A total of 289 PCOM aneurysms in 272 patients modeled with image-based computational fluid dynamics (CFD) were used to construct statistical models using logistic group lasso regression. These models were evaluated with respect to discrimination power and goodness of fit using ten-fold nested cross-validation and a split-sample approach to mimic external validation. RESULTS: The final model retained maximum and minimum wall shear stress (WSS), mean parent artery WSS, maximum and minimum oscillatory shear index, shear concentration index, and aneurysm peak flow velocity; along with aneurysm height and width, bulge location, non-sphericity index, mean Gaussian curvature, angio-architecture type, and patient age. The corresponding area under the curve (AUC) was 0.8359. When omitting data from each of the three largest contributing hospitals in turn, and applying the corresponding model on the left-out data, the AUCs were 0.7507, 0.7081 and 0.5842, respectively. CONCLUSIONS: Statistical models based on a combination of patient age, angio-architecture, hemodynamics and geometric characteristics can discriminate between ruptured and unruptured PCOM aneurysms with an AUC of 84%. It is important to include data from different hospitals to create models of aneurysm rupture that are valid across hospital populations.

PURPOSE: The mechanisms of cerebral aneurysm rupture are not fully understood. We analyzed the associations of hemodynamics, morphology, patient age and gender with aneurysm rupture stratifying by location. METHODS: Using image-based models, 20 hemodynamic and 17 morphological parameters were compared in 1931 ruptured and unruptured aneurysms with univariate logistic regression. Rupture rates were compared between males and females as well as younger and older patients and bifurcation versus sidewall aneurysms for different aneurysm locations. Subsequently, associations between hemodynamics and morphology and patient as well as aneurysm characteristics were analyzed for aneurysms at five locations. RESULTS: Compared to unruptured aneurysms, ruptured aneurysms were characterized by a more irregular shape and were exposed to a more adverse hemodynamic environment described by faster flow, higher wall shear stress, more oscillatory shear, and more unstable and complex flows. These associations with rupture status were consistent for different aneurysm locations. Rupture rates were significantly higher in males at the internal carotid artery (ICA) bifurcation, ophthalmic ICA and the middle cerebral artery (MCA) bifurcation. At the anterior communicating artery (ACOM) and MCA bifurcation, they were significantly higher for younger patients. Bifurcation aneurysms had significantly larger rupture rates at the MCA and posterior communicating artery (PCOM). In these groups with higher rupture rates, aneurysms were characterized by adverse hemodynamics and more complex shapes. CONCLUSION: Hemodynamic and morphological differences between ruptured and unruptured aneurysms are consistent across locations. Adverse morphology and hemodynamics are related to rupture as well as younger age, male gender, and bifurcation aneurysms.