메뉴 건너뛰기




Volumn 19, Issue 4, 2000, Pages 541-561

On the misuses of artificial neural networks for prognostic and diagnostic classification in oncology

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL NEURAL NETWORK; BREAST CANCER; CANCER DIAGNOSIS; CANCER SURVIVAL; CONFERENCE PAPER; HUMAN; MAJOR CLINICAL STUDY; ONCOLOGY; OVARY CANCER; PROBABILITY; PROGNOSIS; RECEIVER OPERATING CHARACTERISTIC; REGRESSION ANALYSIS; STATISTICAL MODEL; STATISTICAL PARAMETERS; UTERINE CERVIX CANCER;

EID: 0034728368     PISSN: 02776715     EISSN: None     Source Type: Journal    
DOI: 10.1002/(SICI)1097-0258(20000229)19:4<541::AID-SIM355>3.0.CO;2-V     Document Type: Conference Paper
Times cited : (208)

References (102)
  • 1
    • 0028788276 scopus 로고
    • Application of artifical neural networks to clinical medicine
    • Baxt WG. Application of artifical neural networks to clinical medicine. Lancet 1995; 346:1135-1138.
    • (1995) Lancet , vol.346 , pp. 1135-1138
    • Baxt, W.G.1
  • 3
    • 0028812688 scopus 로고
    • Artificial neural networks in pathology and medical laboratories
    • Dybowski R, Gant V. Artificial neural networks in pathology and medical laboratories. Lancet 1995; 346:1203-1207.
    • (1995) Lancet , vol.346 , pp. 1203-1207
    • Dybowski, R.1    Gant, V.2
  • 4
    • 0029635662 scopus 로고
    • Nervous about artificial neural networks?
    • Wyatt J. Nervous about artificial neural networks? Lancet 1995; 346:1175-1177.
    • (1995) Lancet , vol.346 , pp. 1175-1177
    • Wyatt, J.1
  • 5
    • 0029840811 scopus 로고    scopus 로고
    • Neural networks in clinical medicine
    • Penny W, Frost D. Neural networks in clinical medicine. Medical Decision Making 1996; 16:386-398.
    • (1996) Medical Decision Making , vol.16 , pp. 386-398
    • Penny, W.1    Frost, D.2
  • 6
    • 84972539015 scopus 로고
    • Neural networks: A review from a statistical perspective (with discussion)
    • Cheng B, Titterington DM. Neural networks: a review from a statistical perspective (with discussion). Statistical Science 1994; 9:2-54.
    • (1994) Statistical Science , vol.9 , pp. 2-54
    • Cheng, B.1    Titterington, D.M.2
  • 7
    • 0002983776 scopus 로고
    • Statistical aspects of neural networks
    • Barndorff-Nielsen OE, Jensen JL (eds). Chapman and Hall: London
    • Ripley BD. Statistical aspects of neural networks. In Networks and Chaos - Statistical and Probabilistic Aspects, Barndorff-Nielsen OE, Jensen JL (eds). Chapman and Hall: London, 1993.
    • (1993) Networks and Chaos - Statistical and Probabilistic Aspects
    • Ripley, B.D.1
  • 9
    • 0003140365 scopus 로고    scopus 로고
    • Neural networks in applied statistics (with discussion)
    • Stern HS. Neural networks in applied statistics (with discussion). Technometrics 1996; 38:205-220.
    • (1996) Technometrics , vol.38 , pp. 205-220
    • Stern, H.S.1
  • 10
    • 0030327681 scopus 로고    scopus 로고
    • Understanding neural networks at statistical tools
    • Warner B, Misra M. Understanding neural networks at statistical tools. American Statistician 1996; 50:284-293.
    • (1996) American Statistician , vol.50 , pp. 284-293
    • Warner, B.1    Misra, M.2
  • 13
    • 0004509186 scopus 로고
    • Regression using fractional polynomials of continuous covariates: Parsimonious parametric modelling (with discussion)
    • Royston P, Altman DG. Regression using fractional polynomials of continuous covariates: parsimonious parametric modelling (with discussion). Applied Statistics 1994; 43:429-467.
    • (1994) Applied Statistics , vol.43 , pp. 429-467
    • Royston, P.1    Altman, D.G.2
  • 14
    • 0000583248 scopus 로고
    • Probabilistic interpretation of feedforward classification network outputs with relationships to statistical pattern recognition
    • Fugleman SF, Hérault J (eds), Springer-Verlag: New York
    • Bridle JS. Probabilistic interpretation of feedforward classification network outputs with relationships to statistical pattern recognition. In Neuro-Computing: Algorithms, Architectures and Applications, Fugleman SF, Hérault J (eds), Springer-Verlag: New York, 1990.
    • (1990) Neuro-computing: Algorithms, Architectures and Applications
    • Bridle, J.S.1
  • 15
    • 0001699291 scopus 로고
    • Training stochastic model recognition algorithms as networks can leads to maximum mutual information estimation of parameters
    • Touretzky DS (ed.), Morgan Kaufmann: San Mateo, CA
    • Bridle JS. Training stochastic model recognition algorithms as networks can leads to maximum mutual information estimation of parameters. In Advances in Neural Information Processing Systems 2, Touretzky DS (ed.), Morgan Kaufmann: San Mateo, CA, 1990.
    • (1990) Advances in Neural Information Processing Systems 2
    • Bridle, J.S.1
  • 16
    • 84995047613 scopus 로고
    • Polytomous logistic regression
    • Engel J. Polytomous logistic regression. Statistica Neerlandica 1988; 42:233-252.
    • (1988) Statistica Neerlandica , vol.42 , pp. 233-252
    • Engel, J.1
  • 20
    • 0024732792 scopus 로고
    • Connectionist learning procedures
    • Hinton GE. Connectionist learning procedures. Artificial Intelligence 1989; 40:185-234.
    • (1989) Artificial Intelligence , vol.40 , pp. 185-234
    • Hinton, G.E.1
  • 24
  • 25
    • 0024866495 scopus 로고
    • On the approximate realization of continuous mappings by neural networks
    • Funahashi K. On the approximate realization of continuous mappings by neural networks. Neural Networks 1989; 2:183-192.
    • (1989) Neural Networks , vol.2 , pp. 183-192
    • Funahashi, K.1
  • 26
    • 0024880831 scopus 로고
    • Multilayer feedforward networks are universal approximators
    • Hornik K, Stinchcombe M, White H. Multilayer feedforward networks are universal approximators Neural Networks 1989; 2:359-366.
    • (1989) Neural Networks , vol.2 , pp. 359-366
    • Hornik, K.1    Stinchcombe, M.2    White, H.3
  • 27
    • 0000756680 scopus 로고
    • The estimation from individual records of the relationship between dose and quantal response
    • Finney DJ. The estimation from individual records of the relationship between dose and quantal response. Biometrika 1947; 34:320-334.
    • (1947) Biometrika , vol.34 , pp. 320-334
    • Finney, D.J.1
  • 29
    • 0011358302 scopus 로고
    • Least-absolute-deviations fits for generalized linear models
    • Morgenthaler S. Least-absolute-deviations fits for generalized linear models. Biometrika 1992; 79:747-754.
    • (1992) Biometrika , vol.79 , pp. 747-754
    • Morgenthaler, S.1
  • 30
    • 0020148757 scopus 로고
    • Resistant fits for some commonly used logistic models with medical applications
    • Pregibon D. Resistant fits for some commonly used logistic models with medical applications. Biometrics 1982; 38:485-498.
    • (1982) Biometrics , vol.38 , pp. 485-498
    • Pregibon, D.1
  • 33
    • 0028044828 scopus 로고
    • Artificial neural networks for cancer research: Outcome prediction
    • Burke HB. Artificial neural networks for cancer research: outcome prediction. Seminars in Surgical Oncology 1994; 10:73-79.
    • (1994) Seminars in Surgical Oncology , vol.10 , pp. 73-79
    • Burke, H.B.1
  • 34
    • 0028019225 scopus 로고
    • Survival analysis of censored data: Neural network analysis detection of complex interactions between variables
    • De Laurentiis M, Ravdin PM. Survival analysis of censored data: neural network analysis detection of complex interactions between variables. Breast Cancer Research and Treatment 1994; 32:113-118.
    • (1994) Breast Cancer Research and Treatment , vol.32 , pp. 113-118
    • De Laurentiis, M.1    Ravdin, P.M.2
  • 35
    • 0028855843 scopus 로고
    • A neural network model for survival data
    • Faraggi D, Simon R. A neural network model for survival data. Statistics in Medicine 1995; 14:73-82.
    • (1995) Statistics in Medicine , vol.14 , pp. 73-82
    • Faraggi, D.1    Simon, R.2
  • 36
    • 0027741408 scopus 로고
    • Neural network analysis to predict treatment outcome
    • Kappen HJ, Neijt JP. Neural network analysis to predict treatment outcome. Annals of Oncology 1993; 4:Supplement S31-34.
    • (1993) Annals of Oncology , vol.4 , Issue.SUPPL.
    • Kappen, H.J.1    Neijt, J.P.2
  • 38
    • 0026794564 scopus 로고
    • A practical application of neural network analysis for predicting outcome of individual breast cancer patients
    • Ravdin PM, Clark GM. A practical application of neural network analysis for predicting outcome of individual breast cancer patients. Breast Cancer Research and Treatement 1992; 22:285-293.
    • (1992) Breast Cancer Research and Treatement , vol.22 , pp. 285-293
    • Ravdin, P.M.1    Clark, G.M.2
  • 41
    • 0001401162 scopus 로고
    • On a correspondence between models in binary regression analysis and in survival analysis
    • Doksum KA, Gasko M. On a correspondence between models in binary regression analysis and in survival analysis. International Statistical Review 1990; 58:243-252.
    • (1990) International Statistical Review , vol.58 , pp. 243-252
    • Doksum, K.A.1    Gasko, M.2
  • 42
    • 0017873866 scopus 로고
    • Regression analysis of grouped survival data with application to breast cancer data
    • Prentice RL, Gloeckler LA. Regression analysis of grouped survival data with application to breast cancer data. Biometrics 1978; 34:57-67.
    • (1978) Biometrics , vol.34 , pp. 57-67
    • Prentice, R.L.1    Gloeckler, L.A.2
  • 47
    • 0031921607 scopus 로고    scopus 로고
    • Feed forward neural networks for the analysis of censored survival data: A partial logistic regression approach
    • Biganzoli E, Boracchi P, Mariani L, Marubini E. Feed forward neural networks for the analysis of censored survival data: a partial logistic regression approach. Statistics in Medicine 1998; 17:1169-1186.
    • (1998) Statistics in Medicine , vol.17 , pp. 1169-1186
    • Biganzoli, E.1    Boracchi, P.2    Mariani, L.3    Marubini, E.4
  • 50
    • 0028544395 scopus 로고
    • Network information criterion - Determining the number of hidden units for artificial neural network models
    • Murata N, Yoshizawa S, Amari SI. Network information criterion - determining the number of hidden units for artificial neural network models. IEEE Transactions on Neural Networks, 1994; 5:865-872.
    • (1994) IEEE Transactions on Neural Networks , vol.5 , pp. 865-872
    • Murata, N.1    Yoshizawa, S.2    Amari, S.I.3
  • 51
    • 0343868221 scopus 로고
    • Receiver operating characteristic (ROC) methodology in artificial neural networks with biomedical applications
    • De Leo JM, Campbell G. Receiver operating characteristic (ROC) methodology in artificial neural networks with biomedical applications. In Proceedings of the World Congress on Neural Networks, 1995.
    • (1995) Proceedings of the World Congress on Neural Networks
    • De Leo, J.M.1    Campbell, G.2
  • 53
    • 0025730090 scopus 로고
    • Nuclear grading of breast carcinoma by image analysis classification by multivariate and neural network analysis
    • Dawson AE, Austin RE, Weinberg DS. Nuclear grading of breast carcinoma by image analysis classification by multivariate and neural network analysis. American Journal of Clinical Pathology 1991; 95:Supplement S29-37.
    • (1991) American Journal of Clinical Pathology , vol.95 , Issue.SUPPL.
    • Dawson, A.E.1    Austin, R.E.2    Weinberg, D.S.3
  • 55
    • 0025942666 scopus 로고
    • Application of neural nets to ultrasound tissue characterization
    • Ostrem JS, Valdes AD, Edmonds PD. Application of neural nets to ultrasound tissue characterization. Ultrasonic Imaging 1991; 13:298-299.
    • (1991) Ultrasonic Imaging , vol.13 , pp. 298-299
    • Ostrem, J.S.1    Valdes, A.D.2    Edmonds, P.D.3
  • 57
    • 0026580903 scopus 로고
    • Application of neural networks to the interpretation of laboratory data in cancer diagnosis
    • Astion ML, Wilding P. Application of neural networks to the interpretation of laboratory data in cancer diagnosis. Clinical Chemistry 1992; 38:34-38.
    • (1992) Clinical Chemistry , vol.38 , pp. 34-38
    • Astion, M.L.1    Wilding, P.2
  • 58
    • 0026604375 scopus 로고
    • Neural networks and diagnosis in the clinical laboratory: State of the art
    • Cicchetti DV. Neural networks and diagnosis in the clinical laboratory: state of the art. Clinical Chemistry 1991; 38:9-10.
    • (1991) Clinical Chemistry , vol.38 , pp. 9-10
    • Cicchetti, D.V.1
  • 59
    • 0026483164 scopus 로고
    • Improvement in specificity of ultrasonography for diagnosis of breast tumors by means of artificial intelligence
    • Goldberg V, Manduca A, Ewert DL, Gisvold JJ, Greenleaf JF. Improvement in specificity of ultrasonography for diagnosis of breast tumors by means of artificial intelligence. Medical Physics 1992; 19:1475-1481.
    • (1992) Medical Physics , vol.19 , pp. 1475-1481
    • Goldberg, V.1    Manduca, A.2    Ewert, D.L.3    Gisvold, J.J.4    Greenleaf, J.F.5
  • 60
    • 0026578134 scopus 로고
    • Introduction of a neuronal network as a tool for diagnostic analysis and classification based on experimental pathologic data
    • Nafe R, Chortiz H. Introduction of a neuronal network as a tool for diagnostic analysis and classification based on experimental pathologic data. Experimental and Toxicologic Pathology 1992; 44:17-24.
    • (1992) Experimental and Toxicologic Pathology , vol.44 , pp. 17-24
    • Nafe, R.1    Chortiz, H.2
  • 61
    • 0026885478 scopus 로고
    • Computer-assisted image interpretation: Use of a neural network to differentiate tubular carcinoma from sclerosing adenosis
    • O'Leary TJ, Mikel UV, Becker RL. Computer-assisted image interpretation: use of a neural network to differentiate tubular carcinoma from sclerosing adenosis. Modern Pathology 1992; 5:402-405.
    • (1992) Modern Pathology , vol.5 , pp. 402-405
    • O'Leary, T.J.1    Mikel, U.V.2    Becker, R.L.3
  • 62
    • 0027293841 scopus 로고
    • Evaluation of laboratory data by conventional statistics and by three types of neural networks
    • Schweiger CR, Soeregi G, Spitzauer S, Maenner G, Pohl AL. Evaluation of laboratory data by conventional statistics and by three types of neural networks. Clinical Chemistry 1993; 39:1966-1971.
    • (1993) Clinical Chemistry , vol.39 , pp. 1966-1971
    • Schweiger, C.R.1    Soeregi, G.2    Spitzauer, S.3    Maenner, G.4    Pohl, A.L.5
  • 63
    • 0028299159 scopus 로고
    • Computer-assisted image classification: Use of neural networks in anatomic pathology
    • Becker RL. Computer-assisted image classification: use of neural networks in anatomic pathology. Cancer Letters 1994; 77:111-117.
    • (1994) Cancer Letters , vol.77 , pp. 111-117
    • Becker, R.L.1
  • 65
    • 0028070813 scopus 로고
    • Increasing the power of surrogate endpoint biomarkers: The aggregation of predictive factors
    • Burke HB. Increasing the power of surrogate endpoint biomarkers: the aggregation of predictive factors. Journal of Cellular Biochemistry 1994; 19 Supplement 278-282.
    • (1994) Journal of Cellular Biochemistry , vol.19 , Issue.SUPPL. , pp. 278-282
    • Burke, H.B.1
  • 69
    • 0028005364 scopus 로고
    • Prediction of breast cancer malignancy using an artificial neural network
    • Floyd Jr CE, JYL, Yun AJ, Sullivan DC, Kornguth PJ. Prediction of breast cancer malignancy using an artificial neural network. Cancer 1994; 74:2944-2948.
    • (1994) Cancer , vol.74 , pp. 2944-2948
    • Ce F., Jr.1    Jyl2    Yun, A.J.3    Sullivan, D.C.4    Kornguth, P.J.5
  • 70
    • 0028296907 scopus 로고
    • Computerized characterization of mammorgraphic masses: Analysis of spiculation
    • Giger ML, Vyborny CJ, Schmidt RA. Computerized characterization of mammorgraphic masses: analysis of spiculation. Cancer Letters 1994; 77:201-211.
    • (1994) Cancer Letters , vol.77 , pp. 201-211
    • Giger, M.L.1    Vyborny, C.J.2    Schmidt, R.A.3
  • 72
    • 0028296903 scopus 로고
    • How to improve a neural network for early detection of hepatic cancer
    • Maclin PS, Dempsey J. How to improve a neural network for early detection of hepatic cancer. Cancer Letters 1994; 77:95-101.
    • (1994) Cancer Letters , vol.77 , pp. 95-101
    • Maclin, P.S.1    Dempsey, J.2
  • 74
    • 0028210335 scopus 로고
    • Artificial neural networks for early detection and diagnosis of cancer
    • Rogers SK, Ruck DW, Kabrisky M. Artificial neural networks for early detection and diagnosis of cancer. Cancer Letters 1994; 77:79-83.
    • (1994) Cancer Letters , vol.77 , pp. 79-83
    • Rogers, S.K.1    Ruck, D.W.2    Kabrisky, M.3
  • 75
    • 0028148549 scopus 로고
    • Artificial neural networks in the diagnosis and prognosis of prostate cancer: A pilot study
    • Snow PB, Smith DS, Catalona WJ. Artificial neural networks in the diagnosis and prognosis of prostate cancer: a pilot study. Journal of Urology 1994; 152:1923-1926.
    • (1994) Journal of Urology , vol.152 , pp. 1923-1926
    • Snow, P.B.1    Smith, D.S.2    Catalona, W.J.3
  • 76
    • 0028219772 scopus 로고
    • Application of backpropagation neural networks to diagnosis of breast and ovarian cancer
    • Wilding P, Morgan MA, Grygotis AE, Shoffner MA, Rosato EF. Application of backpropagation neural networks to diagnosis of breast and ovarian cancer. Cancer Letters 1994; 77:145-153.
    • (1994) Cancer Letters , vol.77 , pp. 145-153
    • Wilding, P.1    Morgan, M.A.2    Grygotis, A.E.3    Shoffner, M.A.4    Rosato, E.F.5
  • 77
    • 0028537379 scopus 로고
    • Reduction of false positives in computerized detection of lung nodules in chest radiographs using artificial neural networks, discriminant analysis, and a rule-based scheme
    • Wu YC, Doi K, Giger ML, Metz CE, Zhang W. Reduction of false positives in computerized detection of lung nodules in chest radiographs using artificial neural networks, discriminant analysis, and a rule-based scheme. Journal of Digital Imaging 1994; 7:196-207.
    • (1994) Journal of Digital Imaging , vol.7 , pp. 196-207
    • Wu, Y.C.1    Doi, K.2    Giger, M.L.3    Metz, C.E.4    Zhang, W.5
  • 79
    • 0029095797 scopus 로고
    • Breast cancer: Prediction with artificial neural network based on bi-rads standardized lexicon
    • Baker JA, Kornguth PJ, Lo JY, Williford ME, Floyd CE. Breast cancer: prediction with artificial neural network based on bi-rads standardized lexicon. Radiology 1995; 196:817-822.
    • (1995) Radiology , vol.196 , pp. 817-822
    • Baker, J.A.1    Kornguth, P.J.2    Lo, J.Y.3    Williford, M.E.4    Floyd, C.E.5
  • 80
    • 0028878836 scopus 로고
    • Use of a neural network and a multiple regression model to predict histologic grade of astrocytoma from mri appearances
    • Christy PS, Tervonen O, Scheithauer BW, Forbes GS. Use of a neural network and a multiple regression model to predict histologic grade of astrocytoma from mri appearances. Neuroradiology 1995; 37:89-93.
    • (1995) Neuroradiology , vol.37 , pp. 89-93
    • Christy, P.S.1    Tervonen, O.2    Scheithauer, B.W.3    Forbes, G.S.4
  • 82
  • 83
    • 0029102693 scopus 로고
    • Solitary pulmonary nodules: Determining the likelihood of malignancy with neural network analysis
    • Gurney JW, Swensen ST. Solitary pulmonary nodules: determining the likelihood of malignancy with neural network analysis. Radiology 1995: 196: 823-829.
    • (1995) Radiology , vol.196 , pp. 823-829
    • Gurney, J.W.1    Swensen, S.T.2
  • 85
    • 0028920449 scopus 로고
    • Automated grading of astrocytomas based on histomorphometric analysis of Ki-67 and Feulgen stained paraffin sections, classification results of neuronal networks and discriminant analysis
    • Kolles H, von Wangenheim A, Vince GH, Niedermayer I, Feiden W. Automated grading of astrocytomas based on histomorphometric analysis of Ki-67 and Feulgen stained paraffin sections, classification results of neuronal networks and discriminant analysis. Annals Cellular Pathology 1995; 8:101-116.
    • (1995) Annals Cellular Pathology , vol.8 , pp. 101-116
    • Kolles, H.1    Von Wangenheim, A.2    Vince, G.H.3    Niedermayer, I.4    Feiden, W.5
  • 86
    • 0028932561 scopus 로고
    • Neural network analysis of quantitative histological factors to predict pathological stage in clinical stage i nonseminomatous testicular cancer
    • Moul JW, Snow PB, Fernandez EB, Maher PD, Sesterhenn IA. Neural network analysis of quantitative histological factors to predict pathological stage in clinical stage i nonseminomatous testicular cancer. Journal of Urology 1995; 153:1674-1677.
    • (1995) Journal of Urology , vol.153 , pp. 1674-1677
    • Moul, J.W.1    Snow, P.B.2    Fernandez, E.B.3    Maher, P.D.4    Sesterhenn, I.A.5
  • 87
    • 0006051824 scopus 로고
    • Commentary on the use of neural networks in clinical urology
    • Niederberger CS. Commentary on the use of neural networks in clinical urology. Journal of Urology 1995; 153:1362.
    • (1995) Journal of Urology , vol.153 , pp. 1362
    • Niederberger, C.S.1
  • 100
    • 0027240597 scopus 로고
    • An international randomized trial comparing four thrombolytic strategies for acute myocardial infarction
    • GUSTO-1 Investigators. An international randomized trial comparing four thrombolytic strategies for acute myocardial infarction. New England Journal of Medicine 1993; 329:673-682.
    • (1993) New England Journal of Medicine , vol.329 , pp. 673-682
  • 101
    • 0030297904 scopus 로고    scopus 로고
    • Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes
    • Tu JV. Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. Journal of Clinical Epidemiology, 1996; 49:1225-1231.
    • (1996) Journal of Clinical Epidemiology , vol.49 , pp. 1225-1231
    • Tu, J.V.1
  • 102
    • 0031893485 scopus 로고    scopus 로고
    • Predicting mortality after coronary artery bypass surgery: What do artifical neural networks learn?
    • Tu JV, Weinstein MC, McNeill BJ, Naylor CD and the Steering Committee of the Cardiac Care Network of Ontario. Predicting mortality after coronary artery bypass surgery: what do artifical neural networks learn? Medical Decision Making 1998; 18:229-235.
    • (1998) Medical Decision Making , vol.18 , pp. 229-235
    • Tu, J.V.1    Weinstein, M.C.2    McNeill, B.J.3    Naylor, C.D.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.