Tumor Growth Kinetics
Tumor Growth Kinetics - Related Videos
07:55
The Influence of Liver Resection on Intrahepatic Tumor Growth
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Cited by 6 •
2016
A high incidence of tumor recurrence after resection of liver metastases remains an unsolved problem. The illustrated mouse model may be useful to investigate the reasons for such recurrences. It combines a liver resection model with intrahepatic tumor cell injection for the first time.
09:14
An In Vitro System to Study Tumor Dormancy and the Switch to Metastatic Growth
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Cited by 39 •
2011
A modified 3-D in vitro system is presented in which growth characteristics of several tumor cell lines in reconstituted basement membrane correlate with the dormant or proliferative behavior of the tumor cells at a metastatic secondary site in vivo.
11:09
Establishing Intracranial Brain Tumor Xenografts With Subsequent Analysis of Tumor Growth and Response to Therapy using Bioluminescence Imaging
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Cited by 104 •
2010
Luciferase-modified human brain tumor xenografts can be established intracranially in athymic mice, with subsequent monitoring of tumor growth and response to therapy using bioluminescence imaging. In combination with survival analysis, bioluminescence monitoring is an essential research tool for pre-clinical testing of therapies being considered for treating brain tumors.
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.
02:08
Bioluminescence Imaging to Assess Tumor Growth in an Organotypic Mouse Brain Slice
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2025
Source: Ciraku, L., et. al. An Ex Vivo Brain Slice Model to Study and Target Breast Cancer Brain Metastatic Tumor Growth. J. Vis. Exp. (2021).This video demonstrates the imaging of luciferase-expressing tumor cells in an organotypic mouse brain slice. Luciferin is added to the medium, and the plate is placed inside the chamber of the in vivo imaging system. Parameters for image capture are adjusted, and the tumor's shape and size are defined. The bioluminescent signals emitted by the tumor...
FAQs
Exponential and Gompertzian models provide different mathematical descriptions of the same longitudinal growth pattern. The choice affects how researchers estimate growth rates and judge whether a curve reflects sustained expansion or changing kinetics over time. Comparing model fits can therefore prevent a single growth-rate value from obscuring important features of tumor progression in a cancer model.
Proliferation and cell loss jointly determine whether tumor size increases, stabilizes, or declines. Nutrient availability can alter this balance, while treatment may change both expansion and loss of tumor cells. Tracking these influences over time helps distinguish a slower-growing tumor from one experiencing treatment-related reduction, which is essential when interpreting response and resistance in cancer experiments.
Doubling time converts longitudinal measurements into an interpretable estimate of how quickly tumor burden increases. It is most informative when calculated from repeated measurements rather than inferred from a single endpoint, because growth rates can change during progression or treatment. Comparing doubling times across tumor models supports quantitative assessment of differences in progression when measurements are collected over comparable intervals.
A temporary reduction in measured tumor size does not necessarily indicate durable therapeutic control. Tumor growth kinetics encourages researchers to examine the subsequent trajectory, including whether growth resumes and how its rate compares with the pretreatment pattern. This longitudinal perspective can reveal delayed progression or resistance that an endpoint measurement would miss, improving interpretation of treatment experiments.
An experimental kinetic analysis begins with repeated tumor-size or cell-number measurements collected across time. Researchers then construct a longitudinal growth curve, estimate growth rates or doubling time, and evaluate an appropriate mathematical model. Finally, they compare trajectories among untreated and treated groups or among tumor models. Consistent measurement timing is important because the analysis depends on change over time.
These measurements can guide experimental design by showing how quickly a model progresses and how long observation should continue. Growth curves help researchers choose informative sampling intervals, compare models with different rates, and avoid relying only on an early or late endpoint. In preclinical cancer research, this improves the ability to detect altered growth and evaluate therapeutic response.
In cancer research, tumor growth kinetics supports comparisons across experimental models and assessment of how interventions alter progression. A treatment may reduce growth without immediately producing a large size decrease, so trajectory-based analysis can capture that effect. Conversely, renewed expansion after an initial response may indicate resistance. These outcomes help connect quantitative tumor behavior with therapeutic development.