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A histopathological classification system of Tyr::NRASQ61K murine melanocytic lesions: A reproducible simplified classification.

Pierre SohierLéa LegrandZackie AktaryChristine GrillVéronique DelmasFlorence BernexEdouard Reyes-GomezLionel LarueBéatrice Vergier
Published in: Pigment cell & melanoma research (2017)
Genetically engineered mouse models offer essential opportunities to investigate the mechanisms of initiation and progression in melanoma. Here, we report a new simplified histopathology classification of mouse melanocytic lesions in Tyr::NRASQ61K derived models, using an interactive decision tree that produces homogeneous categories. Reproducibility for this classification system was evaluated on a panel of representative cases of murine melanocytic lesions by pathologists and basic scientists. Reproducibility, measured as inter-rater agreement between evaluators using a modified Fleiss' kappa statistic, revealed a very good agreement between observers. Should this new simplified classification be adopted, it would create a robust system of communication between researchers in the field of mouse melanoma models.
Keyphrases
  • deep learning
  • machine learning
  • mouse model
  • nuclear factor
  • skin cancer
  • cross sectional
  • toll like receptor