Literature DB >> 16483741

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

Paulo J Lisboa1, Azzam F G Taktak.   

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.

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Mesh:

Year:  2006        PMID: 16483741     DOI: 10.1016/j.neunet.2005.10.007

Source DB:  PubMed          Journal:  Neural Netw        ISSN: 0893-6080


  58 in total

1.  An Evaluation of Artificial Neural Networks in Predicting Pancreatic Cancer Survival.

Authors:  Steven Walczak; Vic Velanovich
Journal:  J Gastrointest Surg       Date:  2017-08-03       Impact factor: 3.452

2.  Intelligent postoperative morbidity prediction of heart disease using artificial intelligence techniques.

Authors:  Nan-Chen Hsieh; Lun-Ping Hung; Chun-Che Shih; Huan-Chao Keh; Chien-Hui Chan
Journal:  J Med Syst       Date:  2010-12-24       Impact factor: 4.460

3.  Training neural network classifiers for medical decision making: the effects of imbalanced datasets on classification performance.

Authors:  Maciej A Mazurowski; Piotr A Habas; Jacek M Zurada; Joseph Y Lo; Jay A Baker; Georgia D Tourassi
Journal:  Neural Netw       Date:  2007-12-27

Review 4.  Application of machine learning algorithms for clinical predictive modeling: a data-mining approach in SCT.

Authors:  R Shouval; O Bondi; H Mishan; A Shimoni; R Unger; A Nagler
Journal:  Bone Marrow Transplant       Date:  2013-10-07       Impact factor: 5.483

5.  Height and Weight Estimation From Anthropometric Measurements Using Machine Learning Regressions.

Authors:  Diego Rativa; Bruno J T Fernandes; Alexandre Roque
Journal:  IEEE J Transl Eng Health Med       Date:  2018-03-29       Impact factor: 3.316

6.  Performance evaluation of radiologists with artificial neural network for differential diagnosis of intra-axial cerebral tumors on MR images.

Authors:  K Yamashita; T Yoshiura; H Arimura; F Mihara; T Noguchi; A Hiwatashi; O Togao; Y Yamashita; T Shono; S Kumazawa; Y Higashida; H Honda
Journal:  AJNR Am J Neuroradiol       Date:  2008-04-03       Impact factor: 3.825

7.  A practical computerized decision support system for predicting the severity of Alzheimer's disease of an individual.

Authors:  Magda Bucholc; Xuemei Ding; Haiying Wang; David H Glass; Hui Wang; Girijesh Prasad; Liam P Maguire; Anthony J Bjourson; Paula L McClean; Stephen Todd; David P Finn; KongFatt Wong-Lin
Journal:  Expert Syst Appl       Date:  2019-04-10       Impact factor: 6.954

8.  Informatics in radiology: comparison of logistic regression and artificial neural network models in breast cancer risk estimation.

Authors:  Turgay Ayer; Jagpreet Chhatwal; Oguzhan Alagoz; Charles E Kahn; Ryan W Woods; Elizabeth S Burnside
Journal:  Radiographics       Date:  2009-11-09       Impact factor: 5.333

9.  Contrast-enhanced ultrasonography parameters in neural network diagnosis of liver tumors.

Authors:  Costin Teodor Streba; Mihaela Ionescu; Dan Ionut Gheonea; Larisa Sandulescu; Tudorel Ciurea; Adrian Saftoiu; Cristin Constantin Vere; Ion Rogoveanu
Journal:  World J Gastroenterol       Date:  2012-08-28       Impact factor: 5.742

10.  Multiproject-multicenter evaluation of automatic brain tumor classification by magnetic resonance spectroscopy.

Authors:  Juan M García-Gómez; Jan Luts; Margarida Julià-Sapé; Patrick Krooshof; Salvador Tortajada; Javier Vicente Robledo; Willem Melssen; Elies Fuster-García; Iván Olier; Geert Postma; Daniel Monleón; Angel Moreno-Torres; Jesús Pujol; Ana-Paula Candiota; M Carmen Martínez-Bisbal; Johan Suykens; Lutgarde Buydens; Bernardo Celda; Sabine Van Huffel; Carles Arús; Montserrat Robles
Journal:  MAGMA       Date:  2008-11-07       Impact factor: 2.310

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