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Predicting Neoadjuvant Treatment Response in Rectal Cancer Using Machine Learning: Evaluation of MRI-Based Radiomic and Clinical Models.

Kent J PetersonMatthew T SimpsonMelissa K DrezdzonAniko SzaboRobin A AusmanAndrew S NenckaPaul M KnechtgesCarrie Y PetersonKirk A LudwigTimothy J Ridolfi
Published in: Journal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract (2022)
The use of a combined model with both clinical and radiomic features resulted in the strongest predictive capability. With the eventual goal of tailoring treatment to the individual, both clinical and radiologic markers offer insight into identifying patients likely to respond favorably to nCRT.
Keyphrases
  • rectal cancer
  • end stage renal disease
  • chronic kidney disease
  • ejection fraction
  • lymph node
  • radiation therapy
  • contrast enhanced
  • patient reported outcomes