Assessing Antiangiogenic Therapy Response by DCE-MRI: Development of a Physiology Driven Multi-Compartment Model Using Population Pharmacometrics

Dynamic contrast enhanced (DCE-) MRI is commonly applied for the monitoring of antiangiogenic therapy in oncology. Established pharmacokinetic (PK) analysis methods of DCE-MRI data do not sufficiently reflect the complex anatomical and physiological constituents of the analyzed tissue. Hence, accepted endpoints such as Ktrans reflect an unknown multitude of local and global physiological effects often rendering an understanding of specific local drug effects impossible. In this work a novel multi-compartment PK model is presented, which for the first time allows the separation of local and systemic physiological effects. DCE-MRI data sets from multiple, simultaneously acquired tissues, i.e. spinal muscle, liver and tumor tissue, of hepatocellular carcinoma (HCC) bearing rats were applied for model development. The full Markov chain Monte Carlo (MCMC) Bayesian analysis method was applied for model parameter estimation and model selection was based on histological and anatomical considerations and numerical criteria. A population PK model (MTL3 model) consisting of 3 measured and 6 latent (unobserved) compartments was selected based on Bayesian chain plots, conditional weighted residuals, objective function values, standard errors of model parameters and the deviance information criterion. Covariate model building, which was based on the histology of tumor tissue, demonstrated that the MTL3 model was able to identify and separate tumor specific, i.e. local, and systemic, i.e. global, effects in the DCE-MRI data. The findings confirm the feasibility to develop physiology driven multi-compartment PK models from DCE-MRI data. The presented MTL3 model allowed the separation of a local, tumor specific therapy effect and thus has the potential for identification and specification of effectors of vascular and tissue physiology in antiangiogenic therapy monitoring

Por que Sêneca escreveu epístolas?

A epistolografia é considerada como um gênero particular da literatura. Presta-se aos mais diferentes desempenhos da linguagem escrita de acordo com as necessidades do homem em razão de sua característica específica: estabelecer comunicação entre pessoas ausentes, como uma das partes de um diálogo. Pode-se, no entanto, esquematizá-la. Sêneca serviu-se deste veículo em suas Epístolas Morais para apresentar os principais temas da filosofia estóica com o vigor de um discurso e de uma teoria renovados

Um deus habita nossa alma (Sêneca, Epístolas morais a Lucílio 41)

Tradução de SÊNECA, Epístolas morais a Lucílio, “Epístola 41

Xplainer: From X-Ray Observations to Explainable Zero-Shot Diagnosis

Automated diagnosis prediction from medical images is a valuable resource to support clinical decision-making. However, such systems usually need to be trained on large amounts of annotated data, which often is scarce in the medical domain. Zero-shot methods address this challenge by allowing a flexible adaption to new settings with different clinical findings without relying on labeled data. Further, to integrate automated diagnosis in the clinical workflow, methods should be transparent and explainable, increasing medical professionals' trust and facilitating correctness verification. In this work, we introduce Xplainer, a novel framework for explainable zero-shot diagnosis in the clinical setting. Xplainer adapts the classification-by-description approach of contrastive vision-language models to the multi-label medical diagnosis task. Specifically, instead of directly predicting a diagnosis, we prompt the model to classify the existence of descriptive observations, which a radiologist would look for on an X-Ray scan, and use the descriptor probabilities to estimate the likelihood of a diagnosis. Our model is explainable by design, as the final diagnosis prediction is directly based on the prediction of the underlying descriptors. We evaluate Xplainer on two chest X-ray datasets, CheXpert and ChestX-ray14, and demonstrate its effectiveness in improving the performance and explainability of zero-shot diagnosis. Our results suggest that Xplainer provides a more detailed understanding of the decision-making process and can be a valuable tool for clinical diagnosis.Comment: 9 pages, 2 figures, 6 table

Exploiting segmentation labels and representation learning to forecast therapy response of PDAC patients

The prediction of pancreatic ductal adenocarcinoma therapy response is a clinically challenging and important task in this high-mortality tumour entity. The training of neural networks able to tackle this challenge is impeded by a lack of large datasets and the difficult anatomical localisation of the pancreas. Here, we propose a hybrid deep neural network pipeline to predict tumour response to initial chemotherapy which is based on the Response Evaluation Criteria in Solid Tumors (RECIST) score, a standardised method for cancer response evaluation by clinicians as well as tumour markers, and clinical evaluation of the patients. We leverage a combination of representation transfer from segmentation to classification, as well as localisation and representation learning. Our approach yields a remarkably data-efficient method able to predict treatment response with a ROC-AUC of 63.7% using only 477 datasets in total

Endothelial FAK is essential for vascular network stability, cell survival, and lamellipodial formation

Morphogenesis of a vascular network requires dynamic vessel growth and regression. To investigate the cellular mechanism underlying this process, we deleted focal adhesion kinase (FAK), a key signaling mediator, in endothelial cells (ECs) using Tie2-Cre mice. Targeted FAK depletion occurred efficiently early in development, where mutants exhibited a distinctive and irregular vasculature, resulting in hemorrhage and lethality between embryonic day (e) 10.5 and 11.5. Capillaries and intercapillary spaces in yolk sacs were dilated before any other detectable abnormalities at e9.5, and explants demonstrate that the defects resulted from the loss of FAK and not from organ failure. Time-lapse microscopy monitoring EC behavior during vascular formation in explants revealed no apparent decrease in proliferation or migration but revealed increases in cell retraction and death leading to reduced vessel growth and increased vessel regression. Consistent with this phenotype, ECs derived from mutant embryos exhibited aberrant lamellipodial extensions, altered actin cytoskeleton, and nonpolarized cell movement. This study reveals that FAK is crucial for vascular morphogenesis and the regulation of EC survival and morphology