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

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@Article{BartschJr:2016:TJU,
  author =       "Georg {Bartsch Jr.} and Anirban P. Mitra and 
                 Sheetal A. Mitra and Arpit A. Almal and Kenneth E. Steven and 
                 Donald G. Skinner and David W. Fry and 
                 Peter F. Lenehan and William P. Worzel and Richard J. Cote",
  title =        "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",
  volume =       "195",
  number =       "2",
  pages =        "493--498",
  year =         "2016",
  ISSN =         "0022-5347",
  DOI =          "doi:10.1016/j.juro.2015.09.090",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0022534715049629",
  abstract =     "Purpose Due to the high recurrence risk of non-muscle
                 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 non muscle 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 tumour. Materials
                 and Methods Whole genome profiling was performed on 112
                 frozen nonmuscle invasive urothelial carcinoma
                 specimens obtained at first presentation on Human WG-6
                 BeadChips (Illumina). A genetic programming algorithm
                 was applied to evolve classifier mathematical models
                 for outcome prediction. Cross-validation based
                 resampling and gene use frequencies were used to
                 identify the most prognostic genes, which were combined
                 into rules used in a voting algorithm to predict the
                 sample target class. Key genes were validated by
                 quantitative polymerase chain reaction. Results The
                 classifier set included 21 genes that predicted
                 recurrence. Quantitative polymerase chain reaction was
                 done for these genes in a subset of 100 patients. A
                 5-gene combined rule incorporating a voting algorithm
                 yielded 77percent sensitivity and 85percent specificity
                 to predict recurrence in the training set, and
                 69percent and 62percent, respectively, in the test set.
                 A singular 3-gene rule was constructed that predicted
                 recurrence with 80percent sensitivity and 90percent
                 specificity in the training set, and 71percent and
                 67percent, respectively, in the test set. Conclusions
                 Using primary nonmuscle invasive urothelial carcinoma
                 from initial occurrences genetic programming identified
                 transcripts in reproducible fashion, which were
                 predictive of recurrence. These findings could
                 potentially impact nonmuscle invasive urothelial
                 carcinoma management.",
  keywords =     "genetic algorithms, genetic programming, urinary
                 bladder neoplasms, neoplasm recurrence, local, genome,
                 algorithms, software",
}

Genetic Programming entries for Georg Bartsch Jr Anirban P Mitra Sheetal A Mitra Arpit A Almal Kenneth E Steven Donald G Skinner David W Fry Peter F Lenehan William P Worzel Richard J Cote

Citations