The use of artificial neural networks in decision support in cancer: A systematic review

Authors:
Paulo J. Lisboa;Azzam F. G. Taktak
Affiliations:
School of Computing and Mathematical Science, Liverpool John Moores University, Liverpool, UK;Department of Clinical Engineering, Royal Liverpool University Hospital, 1st Floor, Duncan Building, Daulby Street, Liverpool L7 8XP, UK
Venue:
Neural Networks
Year:
2006

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Abstract

Artificial neural networks have featured in a wide range of medical journals, often with promising results. This paper reports on a systematic review that was conducted to assess the benefit of artificial neural networks (ANNs) as decision making tools in the field of cancer. The number of clinical trials (CTs) and randomised controlled trials (RCTs) involving the use of ANNs in diagnosis and prognosis increased from 1 to 38 in the last decade. However, out of 396 studies involving the use of ANNs in cancer, only 27 were either CTs or RCTs. Out of these trials, 21 showed an increase in benefit to healthcare provision and 6 did not. None of these studies however showed a decrease in benefit. This paper reviews the clinical fields where neural network methods figure most prominently, the main algorithms featured, methodologies for model selection and the need for rigorous evaluation of results.