Tumor Dynamics
Tumor Dynamics - Related Videos
08:11
Measuring Growth and Gene Expression Dynamics of Tumor-Targeted S. Typhimurium Bacteria
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Cited by 22 •
2013
The goal of these experiments is to generate quantitative time-course data on the growth and gene expression dynamics of attenuated S. typhimurium bacterial colonies growing inside tumors. This video covers tumor cell preparation and implantation, bacteria preparation and injection, whole-animal luminescence imaging, tumor excision, and bacterial colony counting.
08:17
Dynamic Lung Tumor Tracking for Stereotactic Ablative Body Radiation Therapy
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Cited by 9 •
2015
Stereotactic ablative body radiation therapy (SBRT) involves the precise delivery of high-dose radiation to cancer tumor targets. A novel SBRT platform offers a first-of-its-kind gimbaled radiation accelerator mounted within a pivoting O-ring gantry capable of dynamic image-guided tumor tracking. Here, we describe dynamic tumor tracking for lung targets.
12:09
A Novel High-resolution In vivo Imaging Technique to Study the Dynamic Response of Intracranial Structures to Tumor Growth and Therapeutics
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Cited by 14 •
2013
We describe a novel in vivo imaging technique that couples fluorescent chimeric mice with intracranial windows and high-resolution 2-photon microscopy. This imaging platform aids studies of dynamic changes in brain tissue and microvasculature, at a single-cell level, following pathological insults and is adaptable to assess intracranial drug delivery and distribution.
08:24
Extended Time-lapse Intravital Imaging of Real-time Multicellular Dynamics in the Tumor Microenvironment
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Cited by 25 •
2016
This protocol describes the use of multiphoton microscopy to perform extended time-lapse imaging of multicellular interactions in real time, in vivo at single cell resolution.
11:02
In vivo Bioluminescence Imaging of Tumor Hypoxia Dynamics of Breast Cancer Brain Metastasis in a Mouse Model
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Cited by 16 •
2011
Bioluminescence imaging of hypoxia inducible factor-1α activity is applied to monitor intracranial tumor hypoxia development in a breast cancer brain metastasis mouse model.
FAQs
Tumor growth reflects the balance between cancer-cell proliferation and cell death. When proliferation exceeds loss of cells, the tumor may increase in size; changes in that balance can also alter cellular composition and behavior. Tracking these processes helps researchers distinguish simple growth from broader biological changes that may influence progression and treatment response.
Clonal selection can change which cancer-cell populations become more prominent within a tumor. As cellular composition shifts, the tumor may display different behaviors over time, including altered responses to treatment. Examining these changes helps cancer researchers connect evolving cell populations with disease progression and investigate why tumors do not remain biologically uniform.
Tumor cells interact with their surrounding microenvironment, and their spatial organization can change as the disease develops. These relationships provide context for processes such as invasion and metastasis rather than treating the tumor as a uniform mass. Studying spatial patterns therefore helps researchers examine how local organization relates to changing tumor behavior.
Treatment resistance can be investigated as an evolving process rather than only as a final treatment outcome. Longitudinal measurements reveal how tumor size, cellular composition, and behavior change during or after therapy. Combining these observations with genomic analysis and mathematical modeling can help researchers examine patterns associated with resistance and evaluate more precise treatment strategies.
Researchers track tumor dynamics through imaging, genomic analysis, mathematical modeling, and longitudinal measurements. Imaging follows changes in size and spatial organization, while genomic analysis examines cellular differences and evolving composition. Mathematical models help represent these changing processes, and repeated measurements provide a time-based view of progression and treatment response.
This framework is useful when researchers need to connect tumor change with disease progression, invasion, metastasis, or therapy response. It supports analysis of how tumors evolve over time and can help reveal mechanisms of treatment resistance. The resulting information may contribute to predicting outcomes and designing more precise or adaptable cancer therapies.