Tumor Quantification
Tumor Quantification - Related Videos
10:31
The Drosophila Imaginal Disc Tumor Model: Visualization and Quantification of Gene Expression and Tumor Invasiveness Using Genetic Mosaics
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Cited by 11 •
2016
This protocol demonstrates how to generate fluorescently marked, genetically defined clonal tumors in the Drosophila eye/antennal imaginal discs (EAD). It describes how to dissect the EAD and brain from the third instar larvae and how to process them to visualize and quantify gene expression changes and tumor invasiveness.
07:12
Bioluminescence-Based Tumor Quantification Method for Monitoring Tumor Progression and Treatment Effects in Mouse Lymphoma Models
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Cited by 29 •
2016
Bioluminescence imaging is a well-known tool for localizing tumors and metastases, but quantification of these images often requires complex calculations and particular instruments. We describe the easy-to-use luminoscore method, based on precise acquisition conditions, requiring no calculations, and enabling tumor burden and treatment response to be monitored in mouse models.
06:09
Quantification of Tumor Cell Adhesion in Lymph Node Cryosections
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2020
Here, we describe a simple and inexpensive method that allows the quantification of adhesive tumor cells to lymph node (LN) cryosections. LN-adherent tumor cells are readily identified by light microscopy and confirmed by a fluorescence-based method, giving an adhesion index that reveals the tumor cell-binding affinity to LN parenchyma.
11:00
Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment
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Cited by 14 •
2020
Here, we describe a simple and accessible strategy for visualizing, quantifying, and mapping immune cells in formalin-fixed paraffin-embedded tumor tissue sections. This methodology combines existing imaging and digital analysis techniques with the purpose of expanding the multiplexing capability and multiparameter analysis of imaging assays.
02:43
Tumor Implantation and Injection of a Tumor Vaccine in a Mouse Model
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2025
This video demonstrates a cell-based tumor vaccination assay. A mouse, intradermally injected with B16-F10 melanoma cells for tumor formation, is vaccinated with B16-F10 cells secreting Flt3L cytokine near the developed tumor. The secreted Flt3L from the vaccine promotes the maturation and proliferation of dendritic cells that then present tumor antigens to T cells in lymph nodes, triggering a targeted anti-tumor immune response.
FAQs
Reliable tumor quantification begins with consistent identification of the lesion's boundaries. Imaging or histological analysis can be used to distinguish abnormal tissue from surrounding material, after which the selected region supports calculation of a standardized measurement. This segmentation step matters because inconsistent boundaries can change the reported result and weaken comparisons among samples, subjects, or experimental conditions.
Tumor burden can be represented through size, volume, number, or change over time. These measurements describe different aspects of an experimental result, so the selected metric should match the question being investigated. Recording the same measurement consistently allows researchers to compare tumor status across samples or subjects and to evaluate whether an intervention is associated with limited growth.
Repeated assessments add a time-based view that a single measurement cannot provide. Comparing tumor measurements across successive evaluations can reveal growth rate or changes associated with treatment sensitivity. In cancer research, this pattern helps distinguish a tumor that continues growing from one whose progression appears limited under an experimental intervention, supporting more informative treatment-response comparisons.
Standardization makes measurements more comparable across samples, subjects, and experimental conditions. Using consistent approaches to identify abnormal tissue and calculate tumor size, volume, number, or growth supports clearer interpretation of differences between groups. It also strengthens the reproducibility of cancer research outcomes, which is important when researchers evaluate interventions, biomarkers, or therapeutic development strategies.
A basic workflow selects the relevant sample, subject, or experimental model; obtains imaging or histological information; identifies and segments the abnormal tissue; and calculates a standardized tumor measurement. Researchers may then repeat the assessment over time to examine growth or treatment sensitivity. Keeping these steps consistent supports comparisons among experimental conditions and improves result reproducibility.
Researchers apply tumor quantification in preclinical drug studies, biomarker evaluation, and therapeutic development. The resulting measurements help determine whether an intervention limits tumor growth and allow outcomes to be compared across experimental conditions. Depending on the study, imaging, histological analysis, or an experimental model can provide the information needed to characterize treatment response and progression.