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

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

Atlas-Based Interpretable Age Prediction

Age prediction is an important part of medical assessments and research. It can aid in detecting diseases as well as abnormal ageing by highlighting the discrepancy between chronological and biological age. To gain a comprehensive understanding of age-related changes observed in various body parts, we investigate them on a larger scale by using whole-body images. We utilise the Grad-CAM interpretability method to determine the body areas most predictive of a person's age. We expand our analysis beyond individual subjects by employing registration techniques to generate population-wide interpretability maps. Furthermore, we set state-of-the-art whole-body age prediction with a model that achieves a mean absolute error of 2.76 years. Our findings reveal three primary areas of interest: the spine, the autochthonous back muscles, and the cardiac region, which exhibits the highest importance

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

Arterial Pseudoaneurysm within a Pancreatic Pseudocyst

The formation of pancreatic pseudocysts and (pseudo-)aneurysms of intestinal vessels are rare but life-threatening complications in acute and chronic pancreatitis. Here we report the rare case of a patient suffering from chronic pancreatitis with an arterial pseudoaneurysm within a pancreatic pseudocyst and present its successful therapeutic management by angioembolization to prevent critical bleeding. A 67-year-old male with a history of chronic pancreatitis presented with severe acute abdominal pain and vomiting to the emergency department. Seven weeks prior to the present admission, a CT scan had displayed a pancreatic pseudocyst with a maximum diameter of 53 mm. A laboratory examination revealed an elevated white blood cell count (15.40 × 103/μL), as well as elevated serum lipase (191 U/L), bilirubin (1.48 mg/dL), and blood glucose (353 mg/dL) levels. Sonographically, the previously described pancreatic pseudocyst revealed a slightly increased maximum diameter of 65 mm and an inhomogeneous echo of the cystic content. A contrast-enhanced CT scan showed a further increase in maximum diameter to 70 mm of the known pseudocyst. Inside the pseudocyst, a pseudoaneurysm originating from the splenic artery with a maximum diameter of 41 mm was visualized. After interdisciplinary consultation, prophylactic coil embolization of the splenic artery was immediately performed. The pseudoaneurysm was shut off from blood supply by back-door/front-door occlusion employing 27 coils, resulting in complete exclusion of the pseudoaneurysm from the circulation. Pseudoaneurysms are a rare complication of acute and chronic pancreatitis which has been shown to be efficiently treated by coil embolization