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Evaluation of somatic mutations in urine samples as a non-invasive method for the detection and molecular classification of endometrial cancer.

Laura CostasIrene OnievaBeatriz PelegrinaFátima MarinÁlvaro CarmonaMarta López-QuerolJon Frias-GomezPaula Peremiquel-TrillasJosé Manuel MartínezEduard DorcaJoan BrunetMarta PinedaJordi Ponce SebastiàXavier M GuiuSilvia de SanjoséFrancesc Xavier BoschLaia Alemany VilchesSonia Paytubi
Published in: Clinical cancer research : an official journal of the American Association for Cancer Research (2023)
Evaluation of somatic mutations using urine samples may offer a user-friendly and reliable tool for endometrial cancer detection and molecular classification. The diagnostic performance for endometrial cancer detection was very high, and cases could be molecularly classified using these noninvasive and self-collected samples. Additional multi-center evaluations using larger sample sizes are needed to validate the results and understand the potential of urine samples for the early detection and prognosis of endometrial cancer.
Keyphrases
  • endometrial cancer
  • loop mediated isothermal amplification
  • machine learning
  • real time pcr
  • label free
  • deep learning
  • copy number
  • single molecule
  • gene expression
  • risk assessment
  • human health