Effects of Pancreatic Fistula After Minimally Invasive and Open Pancreatoduodenectomy.

Importance: Postoperative pancreatic fistulas (POPF) are the biggest contributor to surgical morbidity and mortality after pancreatoduodenectomy. The impact of POPF could be influenced by the surgical approach.

Objective: To assess the clinical impact of POPF in patients undergoing minimally invasive pancreatoduodenectomy (MIPD) and open pancreatoduodenectomy (OPD).

Unveiling the biological side of PET-derived biomarkers: a simulation-based approach applied to PDAC assessment.

Purpose: Radiomics has revolutionized clinical research by enabling objective measurements of imaging-derived biomarkers. However, the true potential of radiomics necessitates a comprehensive understanding of the biological basis of extracted features to serve as a clinical decision support. In this work, we propose an end-to-end framework for the in silico simulation of [F]FLT PET imaging process in Pancreatic Ductal Adenocarcinoma, accounting for the biological characterization of tissues (including perfusion and fibrosis) on tracer delivery.

Tackling challenges in rare diseases: The ISGACA approach on non-pancreatic cancers in the periampullary region.

Objective: Rare but aggressive cancer types like non-pancreatic periampullary cancers pose unique challenges for cancer research due to their low incidence rates and lack of consensus on optimal treatment strategies, therefore necessitating a collaborative approach. The International Study Group on non-pancreatic peri-Ampullary CAncer (ISGACA) aimed to build a collaborative initiative to pool expertise, funding opportunities, and data from over 60 medical centers, in order to improve outcomes for underrepresented patients with rare cancers.

Methods: The ISGACA approach predefined a stepwise approach including a research scope, establishing a dedicated steering committee, creating a recognizable brand, identifying research gaps, following a well-defined timeline, ensuring robust data collection, addressing legal and ethical considerations, securing financial resources, investing in research ethics training and statistical expertise, raising awareness, creating uniformity, and initiating prospective studies.

Uncertainty quantification and sensitivity analysis of neuron models with ion concentration dynamics.

This paper provides a comprehensive and computationally efficient case study for uncertainty quantification (UQ) and global sensitivity analysis (GSA) in a neuron model incorporating ion concentration dynamics. We address how challenges with UQ and GSA in this context can be approached and solved, including challenges related to computational cost, parameters affecting the system's resting state, and the presence of both fast and slow dynamics. Specifically, we analyze the electrodiffusive neuron-extracellular-glia (edNEG) model, which captures electrical potentials, ion concentrations (Na+, K+, Ca2+, and Cl-), and volume changes across six compartments.

Deep learning-based surrogate models for parametrized PDEs: Handling geometric variability through graph neural networks.

Mesh-based simulations play a key role when modeling complex physical systems that, in many disciplines across science and engineering, require the solution to parametrized time-dependent nonlinear partial differential equations (PDEs). In this context, full order models (FOMs), such as those relying on the finite element method, can reach high levels of accuracy, however often yielding intensive simulations to run. For this reason, surrogate models are developed to replace computationally expensive solvers with more efficient ones, which can strike favorable trade-offs between accuracy and efficiency.