Use of Artificial Intelligence and Machine Learning Algorithms with Gene Expression Profiling to Predict Recurrent Nonmuscle Invasive Urothelial Carcinoma of the Bladder.

Journal: The Journal of urology
PMID: 26459038

Abstract

PURPOSE: Due to the high recurrence risk of nonmuscle invasive urothelial carcinoma it is crucial to distinguish patients at high risk from those with indolent disease. In this study we used a machine learning algorithm to identify the genes in patients with nonmuscle invasive urothelial carcinoma at initial presentation that were most predictive of recurrence. We used the genes in a molecular signature to predict recurrence risk within 5 years after transurethral resection of bladder tumor.

Authors

  • Georg Bartsch
    University of Southern California, Los Angeles, California; Goethe University Frankfurt, Frankfurt, Germany.
  • Anirban P Mitra
    University of Southern California, Los Angeles, California.
  • Sheetal A Mitra
    University of Southern California, Los Angeles, California.
  • Arpit A Almal
    Everist Genomics, Ann Arbor, Michigan.
  • Kenneth E Steven
    University of Copenhagen, Copenhagen, Denmark.
  • Donald G Skinner
    University of Southern California, Los Angeles, California.
  • David W Fry
    Everist Genomics, Ann Arbor, Michigan.
  • Peter F Lenehan
    Everist Genomics, Ann Arbor, Michigan.
  • William P Worzel
    Everist Genomics, Ann Arbor, Michigan.
  • Richard J Cote
    University of Miami, Miami, Florida. Electronic address: RCote@med.miami.edu.

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