Three endpoints of in vivo tumour radiobiology and their statistical estimation
Open accessAbstract
PURPOSE: To review the existing endpoints of tumour growth delay assays in experimental radiobiology with an emphasis on their efficient estimation for statistically significant identification of the treatment effect. To mathematically define doubling time (DT), tumour-growth delay (TGD) and cancer-cell surviving fraction (SF) in vivo using exponential growth and regrowth models with tumour volume measurements obtained from animal experiments. MATERIALS AND METHODS: A statistical model-based approach is used to define and efficiently estimate the three endpoints of tumour therapy in experimental cancer research. RESULTS: The log scale is advocated for plotting the tumour volume data and the respective analysis. Therefore, the geometric mean should be used to display the mean tumour volume data, and the group comparison should be a t-test for the log volume to comply with the Gaussian-distribution assumption. The relationship between cancer-cell SF, TGD and rate of growth is rigorously established. The widespread formula for cell kill is corrected; it has been rigorously shown that TGD is the difference between DTs. The software for the tumour growth delay analysis based on the mixed modeling approach with a complete set of instructions and example can be found on the author's webpage. CONCLUSIONS: The existing practice for TGD data analysis from animal experiments suffers from imprecision and large standard errors that yield low power and statistically insignificant treatment effect. This practice should be replaced with a model-based statistical analysis on the log scale.
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The combination of palbociclib and cetuximab was proposed as a potential strategy to overcome cetuximab resistance by targeting cyclin D1 deregulation.
“The combination of the CDK4/6 inhibitor palbociclib and the EGFR inhibitor cetuximab was proposed as a potential strategy to overcome cetuximab resistance by targeting cyclin D1 deregulation (28).”
From Statistical Assessment of Drug Synergy from <i>In Vivo</i> Combination Studies Using Mouse Tumor Models (2023) · cites this paper as [28]
The Introduction and Discussion describe statistical estimation of tumour-treatment endpoints, while the photodynamic radiotherapy example concerns a mouse prostate-tumour model. Searches for palbociclib, cetuximab resistance, and cyclin D1 found no passage addressing the proposed combination or its rationale in our copy. (LLM, conf=0.78)
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- Authors
- Demidenko, E.
- Affiliations
- Dartmouth College (US)
- Corresponding author
- Demidenko, Eugene
- ORCID
- Demidenko, Eugene
- Venue
- International Journal of Radiation Biology
- Publisher
- Informa UK Limited
- Published
- 2010-02
- Volume
- 86
- Issue
- 2
- Pages
- 164-173
- ISSN
- 0955-3002 (ISSN-L)
- 1362-3095
- PMC
- PMC2900851
- OpenAlex
- W2135761571
- Cited by
- 37
- References
- 31
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 2 · 2023: 5 · 2021: 4 · 2020: 3 · 2019: 3 · 2018: 1 · 2017: 5 · 2016: 2 · 2015: 3 · 2013: 2 · 2012: 1
- FWCI
- 1.37
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- International Journal of Radiation Biology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health
- National Cancer Institute (R01CA130880, P30CA023108)
- Topics
- Physical Sciences › Mathematics › Modeling and Simulation › Mathematical Biology Tumor Growth
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Effects of Radiation Exposure
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Radiation Therapy and Dosimetry
- Keywords
- tumour growth delay, tumor doubling time, surviving fraction, tumor regrowth, exponential growth model, geometric mean, mixed modeling, statistical power, tumor volume
- MeSH terms
- Animals, Cell Survival, Models, Biological, Neoplasms, Experimental, Photochemotherapy, Radiobiology, Models, Statistical, Endpoint Determination
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