Spirgath, K. | Search bioRxiv

bioRxiv2026

Extracellular Matrix Mechanobiology in Pancreatic Ductal Adenocarcinoma: Correlating In Vivo Patient Magnetic Resonance Elastography with Ex Vivo Tissue Mechanics and Histopathology

Pancreatic ductal adenocarcinoma (PDAC) is characterized by a dense desmoplastic extracellular matrix (ECM) that contributes to tumor progression, therapeutic resistance, and poor patient survival. However, the relationship between in vivo imaging-derived mechanical properties, ex vivo tissue biomechanics, ECM architecture, and cellularity remains incompletely understood. Here, we combined pre-operative in vivo clinical magnetic resonance elastography (MRE) with ex vivo biomechanical testing of fresh human PDAC tissue and histopathological analyses. Nine patients undergoing pancreatic resection were prospectively enrolled. Quantitative MRE was performed pre-operatively to assess tissue stiffness through shear wave speed (c) and relative viscosity or fluidity through the loss angle ({varphi}). Fresh tumor and adjacent non-malignant tissue biopsies were subsequently analyzed ex vivo by unconfined uniaxial compression testing to determine elastic moduli and stress relaxation halftime. Histological analyses quantified collagen-rich fibrous tissue area, cell nuclei density, and nuclear morphology. Tumor tissue exhibited significantly increased stiffness and collagen fraction compared with adjacent non-malignant tissue, together with reduced cellularity, smaller nuclear area and more elongated nuclei. Ex vivo stiffness positively correlated with collagen content and negatively correlated with patient survival. Reduced stress relaxation halftime, indicative of increased tissue viscosity, was associated with lower cellularity and elongated nuclei. Importantly, pre-operative MRE parameters of the intact surrounding environment correlated significantly with ex vivo tumor mechanics, cellular organization, and survival. Specifically, a softer and less viscous surrounding environment was associated with stiffer and more viscous tumors, with lower cellularity and elongated nuclei, and poorer prognosis. These findings demonstrate that MRE-derived mechanical biomarkers reflect underlying ECM remodeling and tumor mechanobiology in PDAC. Integrating in vivo imaging with ex vivo tissue mechanics and histopathology may improve non-invasive disease characterization and support biomechanically-informed therapeutic strategies.

bioengineering↗

bioRxiv2026

Automatic deep learning-based segmentation and quantification of stented arterial cross-sections for morphometric analysis

Arterial vascular diseases, such as atherosclerosis, are among the most serious global health threats. In preclinical studies, morphometric analysis of histological arterial cross-sections is considered the gold standard for assessing vascular remodeling and the effectiveness of therapeutic interventions. However, morphometric analysis is usually performed manually, which is time-consuming, subjective, and requires significant user interaction. This paper presents a fully automated, operator-independent framework for the precise morphometric analysis of stented arterial cross-sections, extending the previously developed qHisto (quantitative histology) framework for the quantification of various histological components. A neural network for the segmentation of arterial structures was trained and evaluated using 819 cross-sections. In addition, a quantitative analysis of vascular morphology, fibrin area, and lumen asymmetry was performed using 72 cross-sections from coated and uncoated balloons. The model achieved high segmentation accuracy with a median Dice similarity coefficient of 0.892-0.996. Compared to manual evaluation, the system reduces analysis time by 90%, enabling efficient processing of large datasets. Furthermore, morphometric analysis with qHisto showed significant differences between coated and uncoated balloons, e.g. regarding lumen area (AUC = 0.86) and fibrin ratio (AUC = 0.94). Our developed framework enables fully automated, comprehensive and standardized analysis of histological arterial cross-sections. This helps to reduce time-consuming, repetitive manual assessments and thus facilitates research of disease mechanisms and treatment effects in preclinical studies.

pathology↗