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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.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
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.
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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Assessing eligibility for lung cancer screening using parsimonious ensemble machine learning models: A development

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New ensemble machine learning models predict lung cancer risk in ever-smokers using only three variables. These parsimonious models simplify personalized lung cancer screening eligibility, improving upon current criteria.

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Area of Science:

  • Oncology
  • Machine Learning
  • Public Health

Background:

  • Determining eligibility for risk-based lung cancer screening is challenging.
  • Ensemble machine learning offers a path to develop simple yet effective prediction models.
  • Personalized screening approaches are crucial for widespread adoption.

Purpose of the Study:

  • To develop and validate ensemble machine learning models for lung cancer screening eligibility.
  • To create parsimonious models that maintain high performance with fewer predictors.

Main Methods:

  • Utilized UK Biobank (n=216,714) and US National Lung Screening Trial (NLST, n=26,616) data for model development.
  • Externally validated models in the US Prostate, Lung, Colorectal and Ovarian (PLCO) Screening Trial (n=49,593).
  • Developed models to predict 5-year lung cancer diagnosis and death risk using age, smoking duration, and pack-years.

Main Results:

  • Models predicting lung cancer death (UCL-D) and incidence (UCL-I) used only 3 variables.
  • Achieved comparable or superior discrimination, calibration, and net benefit versus existing models.
  • External validation showed UCL-D AUC of 0.803 and UCL-I AUC of 0.787 in the PLCO trial.
  • Models demonstrated higher sensitivity than USPSTF-2021 criteria at similar specificity.

Conclusions:

  • Parsimonious ensemble machine learning models effectively predict lung cancer risk in ever-smokers.
  • These models offer a simplified approach for implementing risk-based lung cancer screening.
  • The developed models could enhance the personalization and adoption of lung cancer screening programs.