The use of artificial neural networks in decision support in cancer: a systematic review.
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.
School of Computing and Mathematical Science, Liverpool John Moores University, Liverpool, UK.
Paulo J
PJ
Lisboa
Azzam F G
AF
Taktak
D000818:Animals; D002986:Clinical Trials as Topic; D003198:Computer Simulation; D003657:Decision Making; D006801:Humans; D008495:Medical Oncology; D009369:Neoplasms / Q000145:classification / Q000175:diagnosis / Q000628:therapy; D016571:Neural Networks, Computer; D017063:Outcome Assessment, Health Care; D039781:PubMed