Tumor Node Metastasis
Tumor Node Metastasis - Related Videos
12:03
In Vivo Model for Testing Effect of Hypoxia on Tumor Metastasis
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Cited by 6 •
2016
This manuscript describes the development of an animal model that allows for the direct testing of the effects of tumor hypoxia on metastasis and the deciphering the mechanisms of its action. Although the experiments described here focus on Ewing sarcoma, a similar approach can be applied to other tumor types.
06:31
Primary Tumor and MEF Cell Isolation to Study Lung Metastasis
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Cited by 13 •
2015
The goal of this protocol is to study breast tumorigenesis. With this technique, mouse mammary tumors are removed and primary cells are prepared from tumors. A lung extraction protocol is included for studying lung metastasis. Furthermore, another protocol for analyzing mouse embryonic fibroblasts from the mouse embryo is included.
03:23
Modeling Metastatic Endometrial Cancer Model: A Technique to Generate Endometrial Cancer with Lymph Node Metastasis in Rabbit Models
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2023
This video demonstrates the generation of a rabbit model of endometrial cancer by injecting a highly metastatic VX2 cell suspension into the sutured uterine horns of the rabbit. This leads to generation of primary endometrial cancer with retroperitoneal lymph node metastases.
09:53
Labeling of Breast Cancer Patient-derived Xenografts with Traceable Reporters for Tumor Growth and Metastasis Studies
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Cited by 18 •
2016
We describe a method for stable labeling of patient-derived xenografts (PDXs) with lentiviral particles expressing green-fluorescent protein and luciferase reporters. This method allows for tracking the growth of PDXs at the primary site, as well as detecting spontaneous and experimental metastases using in vivo imaging systems.
08:56
Evaluation of Lung Metastasis in Mouse Mammary Tumor Models by Quantitative Real-time PCR
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Cited by 9 •
2016
This protocol describes how to use quantitative Real-time PCR (QRT-PCR) to detect tumor cell specific mRNA representing metastasis within the mouse lung tissue.
FAQs
The three assessments preserve separate information about local tumor behavior, regional lymph-node involvement, and distant spread before they are used together to classify disease severity. This separation helps investigators and clinicians distinguish different patterns of progression rather than relying on a single measurement. The resulting classification can then support prognosis, treatment planning, and consistent comparison among patients.
Separating these categories clarifies whether disease is characterized by growth at the primary site, regional spread, or spread to distant organs. That distinction matters because the extent and pattern of progression influence how disease severity is classified, how prognosis is considered, and how treatment planning is approached. It also prevents different progression patterns from being treated as interchangeable in research analyses.
Clinical examinations, imaging, pathology, and other diagnostic findings provide the evidence used to characterize the three TNM dimensions. Their roles are complementary: findings can describe the primary tumor, indicate regional lymph-node involvement, or identify distant spread. Bringing these sources into one standardized assessment helps translate varied observations into a disease classification that can be communicated and analyzed consistently.
Standardization allows cancer researchers to place patients into comparable disease-severity categories even when studies examine different groups. Researchers can then compare outcomes, analyze how tumor progression relates to survival, and evaluate therapeutic response using a shared staging framework. This common language strengthens interpretation across studies because observed differences can be considered in relation to disease extent rather than described only with local terminology.
Applying the framework begins by collecting relevant clinical examination, imaging, pathology, and other diagnostic findings. The evaluator uses those observations to characterize the primary tumor, regional lymph nodes, and distant spread, then records the corresponding T, N, and M assessments for classification. In clinical care or research, this structured workflow creates a consistent basis for communication, treatment planning, and outcome analysis.
TNM staging is useful when researchers need to interpret outcomes in relation to how far cancer has progressed. Grouping cases by the extent recorded in the T, N, and M assessments supports analysis of prognosis, survival, and therapeutic response. In clinical contexts, the same information contributes to treatment planning, while in research it helps evaluate whether outcomes differ across consistently classified patient groups.