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

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