Neural network analysis of combined conventional and experimental prognostic markers in prostate cancer: A pilot study

British Journal of Cancer

Volumn 78, Issue 2, 1998, Pages 246-250

  Naguib, R N G ^a   Robinson, M C ^b   Neal, D E ^a   Hamdy, F C ^a,b  

^a NEWCASTLE UNIVERSITY   (United Kingdom)

^b FREEMAN HOSPITAL   (United Kingdom)

Author keywords

Apoptosis; Artificial neural networks; bcl 2; Multiple discriminant analysis; p53; Prostate cancer

Indexed keywords

PROTEIN BCL 2; PROTEIN P53; TUMOR MARKER;

ADULT; AGE; AGED; APOPTOSIS; ARTICLE; ARTIFICIAL NEURAL NETWORK; BLOOD CHEMISTRY; BONE SCINTISCANNING; CANCER GRADING; CANCER STAGING; CANCER STATISTICS; CLINICAL ARTICLE; CLINICAL TRIAL; CONTROLLED CLINICAL TRIAL; CONTROLLED STUDY; HUMAN; HUMAN TISSUE; IMMUNOHISTOCHEMISTRY; IMMUNOREACTIVITY; INTERMETHOD COMPARISON; MALE; PILOT STUDY; PRIORITY JOURNAL; PROGNOSIS; PROSTATE CANCER; TREATMENT OUTCOME;

EID: 0031866151     PISSN: 00070920     EISSN: None     Source Type: Journal    
DOI: 10.1038/bjc.1998.472     Document Type: Article

References (23)

1. Andrae A (1993) Neural networks and early diagnosis of myocardial infarction. Lancet 347: 407-408
2. Apakama I, Robinson MC, Walter NM, Charlton RG, Royds JA, Fuller CE, Neal DE and Hamdy FC (1996) bel-2 overexpression combined with p53 protein accumulation correlates with hormone-refractor prostate cancer. Br J Cancer 74: 1258-1262
3. Barnhill SD, Stamey TA, Zhang Z, Zhang H and Madyastha KR (1997) The ability of the ProstAsure™ index to identify prostate cancer patients with low cancer solumes and a high potential for cure. J Urol 157: 241A
4. Brickley MR, Cowpe JG and Shepherd JP (1996) Performance of a computer simulated neural network to categorise normal, premalignant and malignant oral smears. J Oral Pathol Med 25: 424-428
5. Burke HB, Goodman PH, Rosen DB, Henson DE, Weinstein JN, Harrell Jr FE, Marks JR, Winchester DP and Bostwick DG (1997) Artificial neural networks improve the accuracy of cancer survival prediction. Cancer 79: 857-862
6. Byrne RL, Horne CHW, Robinson MC, Autzen P, Apakama I, Bishop RI, Neal DE and Hamdy FC (1997) The expression of WAF-1, p53 and bcl-2 in prostatic adenocarcinoma. Br J Urol 79: 190-195
7. Cox DR (1972) Regression models and life-tables. J Roy Stat Soc (B) 34: 187-200
8. Cross SS, Harrison RF and Kennedy RL (1995) Introduction to neural networks. Lancet 346: 1075-1079
9. De Laurentiis M and Ravdin PM (1994) Survival analysis of censured data: neural network analysis detection of complex interactions between variables. Breast Cancer Res Treat 32: 113-118
10. Dumitra A, Radulescu E and Lazarescu V (1995) Improved classification of psychiatrec mood disorders using a feedforward neural network. Medinfer 8: 818-822
11. Keem S, Meadows H and Kemp H (1995) Hierarchical neural networks in quantitative coronary arteriography. Proc IEEE Int Conf on ANNs 459 464
12. Krongrad A, Granville LJ, Burke MA, Golden RM, Lai S, Cho L and Niederberger CS (1997) Predictors of general quality of life in patients with benign Prostate hyperplasia or prostate cancer. J Urol 157: 534 538
13. Lilford RJ and Braunholtz D (1996) The statistical basis of public policy: a paradigm shift is overdue. Br Med J 313: 603-607
14. Moreno L, Piñero JD, Sànchez JL, Mañas J, Acosta L and Hamilton A (1995) Brain maturation using neural classifier. IEEE Trans Biomed Eng 42: 428-432
15. Naguib RNG and Sherbet GV (1997) Artificial neural networks in cancer research. Pathobiology 65: 129-139
16. Naguib RNG, Adams AE, Horne CHW, Angus B, Sherbet GV and Lennard TWJ (1996) The detection of nodal metastasis in breast cancer using neural nelwork techniques. Phys Meas 17: 297-303
17. Narus SP, Kück K and Westenskow DR (1995) Intelligent monitor for an anaesthesia breathing circuit. Proc Annu Symp Comp Appl Med Care 96-100
18. Office of Population Censuses and Surveys (1996) Cancer Statistics. Registrations 1991 England and Wales. Series MBI No. 20 HMSO: London
19. Schroeder E, Hermanek P, Denis L, Fair W, Gospodarowick MK and Pavone-Macaluso M (1992) The TNM classification of prostate cancer. Prostate 4: 129-138
20. Snow PB, Smith DS and Catalona WJ (1994) Artificial neural networks in the diagnosis and prognosis of prostate cancer: a pilot study. J Urol 152: 1923-1926
21. Stamey TA, Barnhill SD, Zhang Z, Madyastha KR and Zhang H (1997) A neural network (ProstAsure™) with high sensitivity and specificity for diagnosing prostate cancer in men with a PSA < 4.0 ng/ml. J Urol 157: 1425A
22. Stotzka R, Männer R, Bartels PH and Thompson D (1995) A hybrid neural and statistical classifier system for histopathologic grading of prostatic lesions. Analyt Quant Cytol Histol 17: 204-218
23. Wu YC, Doi K and Giger ML (1995) Detection of lung nodules in digital chest radiographs using artificial neural networks: a pilot study. J Dig Intag 8: 88-94