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Multiparametric Modelling of Survival in Pancreatic Ductal Adenocarcinoma Using Clinical, Histomorphological, Genetic and Image-Derived Parameters.

Georgios A KaissisFriederike JungmannSebastian ZiegelmayerFabian K LohöferFelix N HarderAnna Melissa SchlitterAlexander MuckenhuberKatja SteigerRebekka SchirrenHelmut FriessRoland SchmidWilko WeichertMarcus R MakowskiRickmer F Braren
Published in: Journal of clinical medicine (2020)
Imaging-derived features represent an independent survival predictor in PDAC and enable the multiparametric, machine learning-assisted modelling of postoperative overall survival with a high performance compared to clinical and morpho-molecular/genetic parameters. We propose that future studies systematically include imaging-derived features to benchmark their additive value when evaluating biomarker-based model performance.
Keyphrases
  • machine learning
  • high resolution
  • free survival
  • genome wide
  • deep learning
  • patients undergoing
  • copy number
  • artificial intelligence
  • current status
  • mass spectrometry
  • photodynamic therapy
  • case control