Quantitative Imaging in Oncology

High-dimensional imaging datasets pose significant challenges in quantification and interpretation, as traditional analyses often lose critical spatial context and suffer from variability across samples. Our lab applies quantitative imaging, from multiplex confocal microscopy to imaging flow cytometry, to convert biological images into precise, spatially resolved measurements that retain each cell’s location, phenotype, and interactions. We’ve developed colocatome frameworks to map and quantify in situ cellular organization, revealing how microenvironments regulate cell behavior. In parallel, we extract reproducible radiomic features (texture, intensity, shape) and qMRI metrics (e.g., relaxation times) from MRI scans and deploy interpretable machine-learning models to link these imaging biomarkers to clinical outcomes, enabling precise tumor localization and non-invasive monitoring of disease progression.

Quantitative Imaging and Spatial Analysis

We apply quantitative imaging to transform biological images into measurable, interpretable data. From advanced multiplex platforms such as confocal microscopy and imaging flow cytometry, we obtain high-dimensional spatial and phenotypic profiles of cells in their native tissue context. Unlike bulk or sequencing-based approaches, this imaging allows us to retain crucial information on cell location, identity, and interaction.

A major component of our spatial analysis pipeline is the development of colocatome frameworks, which quantify the organization and interaction of cell populations in situ. For example, we investigate the spatial positioning of hematopoietic stem cells (HSCs) within bone marrow niches using high-resolution microscopy. Understanding these spatial relationships is essential for revealing how microenvironments regulate stem cell behavior and lineage commitment.

Radiomics and Quantitative MRI

Our imaging research also includes radiological data analysis, with a focus on transitioning from qualitative interpretation to quantitative, reproducible metrics. In radiomics, we extract features such as texture, intensity, and shape from defined regions of interest within MRI scans, and use machine learning models to link these features to clinical outcomes. We emphasize interpretability and robustness, testing models on controlled environments to avoid confounding due to real-patient variability and ethical exposure limitations.

In parallel, we employ quantitative MRI (qMRI) techniques to move beyond traditional contrast-based imaging. By measuring intrinsic physical properties of tissues, e.g. relaxation times, we obtain microstructural insights into tissue composition, particularly in brain imaging. Combining qMRI with machine learning enables precise localization of tumors and assessment of disease progression, further enhancing the clinical utility of radiological data.