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Related Concept Videos

Tumor Progression02:07

Tumor Progression

Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...
Tumor Progression02:07

Tumor Progression

Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...
Cancer02:18

Cancer

Cancers arise due to mutations in genes involved in the regulation of cell division, which leads to unrestricted cell proliferation. Modern science and medicine have made great strides in the understanding and treatment of cancer, including eradicating cancer in some patients. However, there is still no cure for cancer. This is largely due to the fact that cancer is a large group of many diseases.
Adaptive Mechanisms in Cancer Cells02:53

Adaptive Mechanisms in Cancer Cells

Cancer cells accumulate genetic changes at an abnormally rapid rate due to the defects in the DNA repair mechanisms. From an evolutionary perspective, such genetic instability is advantageous for cancer development. Mutant cell lines accumulate a series of beneficial mutations that contribute to their progression into cancer.
Some of the advantages that cancer cells have on normal cells include - enhanced ability to divide without terminally differentiating, induce new blood vessel formation,...
Cancer Survival Analysis01:21

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
Abnormal Proliferation02:23

Abnormal Proliferation

Under normal conditions, most adult cells remain in a non-proliferative state unless stimulated by internal or external factors to replace lost cells. Abnormal cell proliferation is a condition in which the cell's growth exceeds and is uncoordinated with normal cells. In such situations, cell division persists in the same excessive manner even after cessation of the stimuli, leading to persistent tumors. The tumor arises from the damaged cells that replicate to pass the damage to the daughter...

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Related Experiment Video

Updated: Jun 9, 2026

Multidimensional Coculture System to Model Lung Squamous Carcinoma Progression
07:53

Multidimensional Coculture System to Model Lung Squamous Carcinoma Progression

Published on: March 17, 2020

Lung cancer growth dynamics.

Jerome M Reich1, Jong-Sung Kim

  • 1Thoracic Oncology Program, Earle A Chiles Research Center, Department of Mathematics and Statistics, Portland State University, Portland, OR 97225-7007, United States. Reichje@dnamail.com

European Journal of Radiology
|September 3, 2010
PubMed
Summary
This summary is machine-generated.

Mathematical modeling reveals early lung cancer diagnosis via screening is difficult. Tumor growth dynamics show diameter increases exponentially with volume doublings, while volume increases quadratically with radius.

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Utilizing 18F-FDG PET/CT Imaging and Quantitative Histology to Measure Dynamic Changes in the Glucose Metabolism in Mouse Models of Lung Cancer
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Published on: July 21, 2018

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Multidimensional Coculture System to Model Lung Squamous Carcinoma Progression
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Utilizing 18F-FDG PET/CT Imaging and Quantitative Histology to Measure Dynamic Changes in the Glucose Metabolism in Mouse Models of Lung Cancer
06:51

Utilizing 18F-FDG PET/CT Imaging and Quantitative Histology to Measure Dynamic Changes in the Glucose Metabolism in Mouse Models of Lung Cancer

Published on: July 21, 2018

Area of Science:

  • Oncology
  • Mathematical Biology
  • Radiology

Background:

  • Lung cancer remains a leading cause of cancer death.
  • Early detection is crucial for improving patient outcomes.
  • Current screening methods face limitations in detecting early-stage, lethal lung cancer.

Purpose of the Study:

  • To model lung cancer growth dynamics using mathematical principles.
  • To assess the feasibility of early diagnosis through radiographic or CT screening.
  • To understand the relationship between tumor size, volume, and growth rate.

Main Methods:

  • Development of mathematical models for lung cancer tumor growth.
  • Analysis of tumor diameter and volume changes over time.
  • Estimation of tumor volume doubling time using prevalence:incidence ratios from clinical trials.

Main Results:

  • Early diagnosis of lethal lung cancer via screening is mathematically unattainable.
  • Tumor diameter increases exponentially with volume doublings (base 1.26).
  • Tumor volume increases quadratically with radius.
  • Mean tumor volume doubling time can be estimated from prevalence:incidence ratios, with Stage IA example at 230 days.

Conclusions:

  • Mathematical modeling provides critical insights into lung cancer natural history.
  • Screening's ability to detect lethal lung cancer early is limited by growth dynamics.
  • The methodology helps discern minimal diameter changes indicative of volume growth.
  • Tumor volume doubling time estimation is feasible with high compliance in large trials.