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Related Concept Videos

Mouse Models of Cancer Study02:43

Mouse Models of Cancer Study

Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
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Mouse Models of Cancer Study02:43

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Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...

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Related Experiment Video

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An Orthotopic Murine Model of Human Prostate Cancer Metastasis
06:48

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Published on: September 18, 2013

Prostate cancer and computer models: Background, limitations, and potential.

R Etzioni1, I M Thompson

  • 1Biostatistics, Fred Hutchinson Cancer Research Center, Seattle, Washington and Urology Service, Brooke Army Medical Center Fort Cam Houston, Texas, USA.

Urologic Oncology
|January 13, 2011
PubMed
Summary
This summary is machine-generated.

Computer models for localized prostate cancer decision-making show promise but require better data and design. These models augment, but do not replace, clinical trials for treatment decisions.

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Area of Science:

  • Oncology
  • Medical Informatics
  • Decision Science

Background:

  • Localized prostate cancer treatment decisions are complex.
  • Computer models aim to aid these decisions.
  • Previous models have limitations.

Purpose of the Study:

  • To review and evaluate decision-making models for localized prostate cancer.
  • To identify strengths and weaknesses of existing models.
  • To suggest improvements for future modeling efforts.

Main Methods:

  • Literature review of the past five years.
  • Sequential description and evaluation of four decision-making models.
  • Analysis of model design and data utilization.

Main Results:

  • Four distinct models for localized prostate cancer decision-making were identified.
  • All models suffer from a lack of unbiased natural history data.
  • Model design flaws and data use issues were prevalent.

Conclusions:

  • Despite limitations, significant progress has been made in decision modeling.
  • Improved data acquisition and model logical structure are crucial.
  • Computer models serve as a valuable adjunct to, not a replacement for, clinical trials.