Bayesian pharmacokinetic modeling of dynamic contrast-enhanced magnetic resonance imaging: validation and application

Tracer-kinetic analysis of dynamic contrast-enhanced magnetic resonance imaging data is commonly performed with the well-known Tofts model and nonlinear least squares (NLLS) regression. This approach yields point estimates of model parameters, uncertainty of these estimates can be assessed e.g. by an additional bootstrapping analysis. Here, we present a Bayesian probabilistic modeling approach for tracer-kinetic analysis with a Tofts model, which yields posterior probability distributions of perfusion parameters and therefore promises a robust and information-enriched alternative based on a framework of probability distributions. In this manuscript, we use the quantitative imaging biomarkers alliance (QIBA) Tofts phantom to evaluate the Bayesian tofts model (BTM) against a bootstrapped NLLS approach. Furthermore, we demonstrate how Bayesian posterior probability distributions can be employed to assess treatment response in a breast cancer DCE-MRI dataset using Cohen's d. Accuracy and precision of the BTM posterior distributions were validated and found to be in good agreement with the NLLS approaches, and assessment of therapy response with respect to uncertainty in parameter estimates was found to be excellent. In conclusion, the Bayesian modeling approach provides an elegant means to determine uncertainty via posterior distributions within a single step and provides honest information about changes in parameter estimates

Metabolic Profiling of Alpine and Ecuadorian Lichens

Non-targeted H-1-NMR methods were used to determine metabolite profiles from crude extracts of Alpine and Ecuadorian lichens collected from their natural habitats. In control experiments, the robustness of metabolite detection and quantification was estimated using replicate measurements of Stereocaulon alpinum extracts. The deviations in the overall metabolite fingerprints were low when analyzing S. alpinum collections from different locations or during different annual and seasonal periods. In contrast, metabolite profiles observed from extracts of different Alpine and Ecuadorian lichens clearly revealed genus- and species-specific profiles. The discriminating functions determining cluster formation in principle component analysis (PCA) were due to differences in the amounts of genus-specific compounds such as sticticin from the Sticta species, but also in the amounts of ubiquitous metabolites, such as sugar alcohols or trehalose. However, varying concentrations of these metabolites from the same lichen species e.g.,due to different environmental conditions appeared of minor relevance for the overall cluster formation in PCA. The metabolic clusters matched phylogenetic analyses using nuclear ribosomal DNA (nrDNA) internal transcribed spacer (ITS) sequences of lichen mycobionts, as exemplified for the genus Sticta. It can be concluded that NMR-based non-targeted metabolic profiling is a useful tool in the chemo-taxonomy of lichens. The same approach could also facilitate the discovery of novel lichen metabolites on a rapid and systematical basis

Integration of clinical parameters and CT-based radiomics improves machine learning assisted subtyping of primary hyperaldosteronism

ObjectivesThe aim of this study was to investigate an integrated diagnostics approach for prediction of the source of aldosterone overproduction in primary hyperaldosteronism (PA).Methods269 patients from the prospective German Conn Registry with PA were included in this study. After segmentation of adrenal glands in native CT images, radiomic features were calculated. The study population consisted of a training (n = 215) and a validation (n = 54) cohort. The k = 25 best radiomic features, selected using maximum-relevance minimum-redundancy (MRMR) feature selection, were used to train a baseline random forest model to predict the result of AVS from imaging alone. In a second step, clinical parameters were integrated. Model performance was assessed via area under the receiver operating characteristic curve (ROC AUC). Permutation feature importance was used to assess the predictive value of selected features.ResultsRadiomics features alone allowed only for moderate discrimination of the location of aldosterone overproduction with a ROC AUC of 0.57 for unilateral left (UL), 0.61 for unilateral right (UR), and 0.50 for bilateral (BI) aldosterone overproduction (total 0.56, 95% CI: 0.45-0.65). Integration of clinical parameters into the model substantially improved ROC AUC values (0.61 UL, 0.68 UR, and 0.73 for BI, total 0.67, 95% CI: 0.57-0.77). According to permutation feature importance, lowest potassium value at baseline and saline infusion test (SIT) were the two most important features.ConclusionIntegration of clinical parameters into a radiomics machine learning model improves prediction of the source of aldosterone overproduction and subtyping in patients with PA

Finding Homogeneity in Heterogeneity—A New Approach to Quantifying Landscape Mosaics Developed for the Lao PDR

A key challenge for land change science in general and research on swidden agriculture in particular, is linking land cover information to human–environment interactions over larger spatial areas. In Lao PDR, a country facing rapid and multi-level land change processes, this hinders informed policy- and decision-making. Crucial information on land use types and people involved is still lacking. This article proposes an alternative approach for the description of landscape mosaics. Instead of analyzing local land use combinations, we studied land cover mosaics at a meso-level of spatial scale and interpreted these in terms of human–environmental interactions. These landscape mosaics were then overlaid with population census data. Results showed that swidden agricultural landscapes, involving 17% of the population, dominate 29% of the country, while permanent agricultural landscapes involve 74% of the population in 29% of the territory. Forests still form an important component of these landscape mosaics

Molecular Mechanisms to Target Cellular Senescence in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) has emerged as a major cause of cancer-related death and is the most common type of liver cancer. Due to the current paucity of drugs for HCC therapy there is a pressing need to develop new therapeutic concepts. In recent years, the role of Serum Response Factor (SRF) and its coactivators, Myocardin-Related Transcription Factors A and B (MRTF-A and -B), in HCC formation and progression has received considerable attention. Targeting MRTFs results in HCC growth arrest provoked by oncogene-induced senescence. The induction of senescence acts as a tumor-suppressive mechanism and therefore gains consideration for pharmacological interventions in cancer therapy. In this article, we describe the key features and the functional role of senescence in light of the development of novel drug targets for HCC therapy with a focus on MRTFs

Evaluation of the clinical utility of maximum intensity projections of 3D contrast-enhanced, T1-weighted imaging for the detection of brain metastases

BACKGROUND To visualize and assess brain metastases on magnetic resonance imaging, radiologists face an ever-increasing pressure to perform faster and more efficiently. The usage of maximum intensity projections (MIPs) of contrast-enhanced T1-weighed (T1ce) magnetization-prepared rapid acquisition with gradient echo (MP-RAGE) images proposes to increase reading efficiency by increasing lesion conspicuity while reducing in the number of images to be reviewed. AIM To assess if MIPs save reading time and achieve the same level of diagnostic accuracy as standard 1 mm T1ce images for the detection of brain metastases. METHODS Forty-four patients were included in this retrospective study. Axial reformations of T1ce MP-RAGE (TR/TE = 2300/2.25 ms, resolution = 1 mm$^{3}$ ) images were analyzed and post-processed into 5 and 10 mm MIPs. Two readers evaluated the randomly assorted images and recorded reading time. Reading time differences were analyzed using the Wilcoxon test, and inter-reader statistics were performed using Bland-Altman plots. RESULTS About 22.5 61.2 s/study and 43.8 ± 159.9 s/study were saved using 5 and 10 mm MIPs, respectively. Combined average sensitivity was 92.0% for 5 mm MIPs and 86.3% for 10 mm MIPs compared to standard 1 mm axial slices, with an average rate of 0.98 and 0.57 false positives per study, respectively CONCLUSION: While 5 mm and 10 mm T1ce MP-RAGE MIPs showed a clinical benefit in reducing reading times for evaluation of brain metastases, they should be used in conjunction with standard 1 mm images for best sensitivity and specificity, a practice which possibly annuls their benefit

Evaluation of the clinical utility of maximum intensity projections of 3D contrast‐enhanced

BACKGROUND To visualize and assess brain metastases on magnetic resonance imaging, radiologists face an ever-increasing pressure to perform faster and more efficiently. The usage of maximum intensity projections (MIPs) of contrast-enhanced T1-weighed (T1ce) magnetization-prepared rapid acquisition with gradient echo (MP-RAGE) images proposes to increase reading efficiency by increasing lesion conspicuity while reducing in the number of images to be reviewed. AIM To assess if MIPs save reading time and achieve the same level of diagnostic accuracy as standard 1 mm T1ce images for the detection of brain metastases. METHODS Forty-four patients were included in this retrospective study. Axial reformations of T1ce MP-RAGE (TR/TE = 2300/2.25 ms, resolution = 1 mm$^{3}$ ) images were analyzed and post-processed into 5 and 10 mm MIPs. Two readers evaluated the randomly assorted images and recorded reading time. Reading time differences were analyzed using the Wilcoxon test, and inter-reader statistics were performed using Bland-Altman plots. RESULTS About 22.5 61.2 s/study and 43.8 ± 159.9 s/study were saved using 5 and 10 mm MIPs, respectively. Combined average sensitivity was 92.0% for 5 mm MIPs and 86.3% for 10 mm MIPs compared to standard 1 mm axial slices, with an average rate of 0.98 and 0.57 false positives per study, respectively CONCLUSION: While 5 mm and 10 mm T1ce MP-RAGE MIPs showed a clinical benefit in reducing reading times for evaluation of brain metastases, they should be used in conjunction with standard 1 mm images for best sensitivity and specificity, a practice which possibly annuls their benefit