Evidence & Publications

At Cairina, our commitment to precision oncology is backed by cutting-edge research and validated methodologies. Our proprietary localized convolutional function regression (LCFR) technology transforms dynamic contrast-enhanced MRI (DCE-MRI) data into detailed, non-invasive maps of interstitial fluid dynamics, offering unparalleled insights into tumor physiology.

DOI: https://doi.org/10.1371/journal.pcbi.1012106
Published: May 15, 2024

This study examines the accuracy and repeatability of parameter estimation in nested compartment models used for analyzing contrast transport in dynamic contrast-enhanced MRI. While these models are structurally identifiable, their practical identifiability depends on data quality and noise levels. Using both artificial and real data, the study demonstrates that increased noise reduces identifiability, but this can be mitigated by improving data quality. The findings apply broadly, regardless of tissue characteristics or contrast agent enhancement patterns.

DOI: https://doi.org/10.1063/5.0190561
Published: April 29, 2024

This study introduces a new method for analyzing DCE-MRI data using localized convolutional function regression, allowing for simultaneous measurement of interstitial fluid velocity, diffusion, and perfusion in 3D. The approach was validated both computationally and experimentally, demonstrating accurate fluid dynamics measurement in situ and in vivo. When applied to human MRIs, it revealed tissue-specific differences, notably higher fluid velocity in breast cancer compared to brain cancer. This improved strategy enhances the study of interstitial transport in tumors, contributing to a deeper understanding of cancer progression and treatment response.

DOI: https://doi.org/10.1002/wsbm.1582
Published: Aug. 23, 2022

This article explores the role of interstitial fluid (IF) and cerebrospinal fluid (CSF) in brain function, highlighting their roles in nutrient transport, waste removal, and protection of brain cells. IF moves via pressure-driven bulk flow, draining into CSF, which circulates through perivascular spaces and facilitates waste clearance. Changes in fluid composition, flow, and volume can contribute to brain dysfunction and disease progression, including cancer and neurodegenerative disorders.

DOI: https://doi.org/10.1063/1.5023503
Published: June 26, 2018

Glioblastoma (GBM) is a highly invasive brain tumor, and interstitial fluid flow (IFF) plays a key role in driving its spread. This study introduces a novel, noninvasive method using dynamic contrast-enhanced MRI to measure IFF velocities in glioma models, providing clinically translatable insights. Results reveal that IFF direction is highly heterogeneous—not always outward—and velocity magnitudes remain consistent across different GBM xenografts, independent of tumor size. This breakthrough technique advances our understanding of GBM fluid dynamics and offers a powerful tool for both research and clinical applications.

DOI: http://dx.doi.org/10.2147/CMAR.S65444 
Published: Aug. 19, 2014

This article examines the role of interstitial flow (IF) in cancer progression and therapy. Once considered a passive transport mechanism, IF is now recognized as a promigratory force that drives cancer cell invasion and contributes to therapeutic resistance and recurrence. Increased intratumoral pressure and outward IF toward healthy tissue are hallmarks of cancer progression, influencing both tumor dynamics and treatment outcomes. While research has largely focused on IF’s impact on drug transport, this review explores its broader role in tumor microenvironment interactions, therapeutic failure, and treatment resistance, offering insights into potential new strategies for cancer therapy.

LCFR-measured perfusion is correlated with Evans Blue coverage in vivo. (a)–(d) Representative coronal IHC stains through the central tumor slice (top row) and MR resolution-matching intensity projection (bottom row), consisting of DAPI (a), GFP-expressing GL261 cells (b), Evans Blue (c). (d) Merge of all IHC demonstrating tumor heterogeneity. (e) Estimated perfusion field, ξtrans⁠, overlaid on post-contrast T1-weighted image. (f) Scatter plot depicting the correlation and 95% confidence interval of linear regression between mean tumor perfusion as measured by DCE-MRI (⁠ξtrans⁠) and Evans Blue Coverage (mean tumor stain intensity), for N= 17 histology slices. (g) 3D velocity vector field of estimated interstitial velocity ξu→⁠, overlaid on the 3D T1 post-contrast volume. (h) and (i) Violin plots depicting the estimated velocity magnitude (h) and perfusion (i) for six animals imaged 7 and 14 days after tumor implantation. (Read Study)

At Cairina, we aim to revolutionize oncology care by providing clinicians with tools that enable personalized, effective, and accessible treatment planning.