The use of artificial neural network analysis to improve the predictive accuracy of prostate biopsy in the Japanese population

Japanese Journal of Clinical Oncology

Volumn 34, Issue 10, 2004, Pages 602-607

  Matsui, Yoshiyuki ^a,c   Utsunomiya, Noriaki ^a   Ichioka, Kentaro ^a   Ueda, Norihumi ^a   Yoshimura, Koji ^a   Terai, Akito ^a   Arai, Yoichi ^b  

^a KURASHIKI CENTRAL HOSPITAL   (Japan)

^b TOHOKU UNIVERSITY SCHOOL OF MEDICINE   (Japan)

^c KYOTO UNIVERSITY   (Japan)

Author keywords

Artificial neural network analysis; Logistic regression analysis; Prostate biopsy; Prostate cancer

Indexed keywords

PROSTATE SPECIFIC ANTIGEN;

ADULT; AGED; ANALYTICAL PARAMETERS; AREA UNDER THE CURVE; ARTICLE; ARTIFICIAL NEURAL NETWORK; CANCER PATIENT; CANCER SCREENING; CONTROLLED STUDY; DIAGNOSTIC ACCURACY; DIGITAL RECTAL EXAMINATION; HUMAN; JAPAN; LOGISTIC REGRESSION ANALYSIS; MAJOR CLINICAL STUDY; MALE; POPULATION RESEARCH; PREDICTION; PROSTATE BIOPSY; PROSTATE CANCER; RECEIVER OPERATING CHARACTERISTIC; SENSITIVITY ANALYSIS; STATISTICAL SIGNIFICANCE; BIOPSY; BLOOD; PATHOLOGY; PREDICTION AND FORECASTING; PROSTATE; PROSTATE TUMOR; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL;

AGED; BIOPSY; HUMANS; LOGISTIC MODELS; MALE; NEURAL NETWORKS (COMPUTER); PREDICTIVE VALUE OF TESTS; PROSTATE; PROSTATE-SPECIFIC ANTIGEN; PROSTATIC NEOPLASMS; SENSITIVITY AND SPECIFICITY;

EID: 16644395527     PISSN: 03682811     EISSN: None     Source Type: Journal    
DOI: 10.1093/jjco/hyh112     Document Type: Article

References (27)

1. Polascik TJ, Oesterling JE, Partin AW. Prostate specific antigen: a decade of discovery-what we have learned and where we are going. J Urol 1999;162:293-306.
2. Brawer MK, Cheli CD, Neaman IE, Goldblatt J, Smith C, Schwartz MK et al. Complexed prostate specific antigen provides significant enhancement of specificity compared with total prostate specific antigen for detecting prostate cancer. J Urol 2000;163:1476-80.
3. Horninger W, Reissigl A, Klocker H, Rogatsch H, Fink K, Strasser H, Bartsch G. Improvement of specificity in PSA-based screening by using PSA-transition zone density and percent free PSA in addition to total PSA levels. Prostate 1998;37:133-7.
4. Maeda H, Arai Y, Ishitoya S, Okubo K, Aoki Y, Okada T. Prostate specific antigen adjusted for the transition zone volume as an indicator of prostate cancer. J Urol 1997;158:2193-6.
5. Catalona WJ, Southwick PC, Slawin KM, Partin AW, Brawer MK, Flanigan RC et al. Comparison of percent free PSA, PSA density, and age-specific PSA cutoffs for prostate cancer detection and staging. Urology 2000;56:255-260.
6. Ciatto S, Bonardi R, Lombardi C, Zappa M, Gervasi G and Cappelli G. Analysis of PSA velocity in 1666 healthy subjects undergoing total PSA determination at two consecutive screening rounds, Int J Biol Markers 2002; 17:79-83.
7. Djavan B, Remzi M, Zlotta AR, Ravery V, Hammerer P, Reissigl A et al. Complexed prostate-specific antigen, complexed prostate-specific antigen density of total and transition zone, complexed/total prostate-specific antigen ratio, free-to-total prostate-specific antigen ratio, density of total and transition zone prostate-specific antigen: results of the prospective multicenter European trial. Urology 2002;60 (Suppl 4A):4-9.
8. Marks LS, Llanes AS, Linton HJ, Gasior CL, Millar LS, Mikolajczyk SD et al. BPSA is a potential serum marker for benign prostatic hyperplasia (BPH). J Urol 2001;165 (Suppl):266.
9. Nam RK, Diamandis EP, Toi A, Trachtenberg J, Magklara A, Scorilas A et al. Serum human glandular kallikrein-2 prostate levels predict the presence of prostate cancer among men with elevated prostate-specific antigen. J Clin Oncol 2000; 18:1036-1042.
10. Khan MA, Partin AW, Rittenhouse HG, Mikolajczyk SD, Sokoll LJ, Chan DW et al. Evaluation of proprostate specific antigen for early detection of prostate cancer in men with a total prostate specific antigen range of 4.0 to 10.0 ng/ml. J Urol 2003;170:723-6.
11. Egawa S, Soh S, Ohori M, Uchida T, Gohji K, Fujii A et al. The ratio of free to total serum prostate specific antigen and its use in differential diagnosis of prostate carcinoma in Japan. Cancer 1997;79:90-8.
12. Kuriyama M, Abrahamsson PA, Imai K, Akimoto S, Deguchi N, Shichiri Y et al. Determination of serum prostate-specific antigen-alphal-antichymotrypsin complex for diagnosis of prostate cancer in Japanese cases. Scand J Urol Nephrol 2001;35:5-10.
13. Okihara K, Ukimura O, Nakamura T, Mizutani Y, Kawauchi A, Naya Y et al. Can complexed prostate specific antigen enhance prostate cancer detection in Japanese men? Eur Urol 2004;46:57-64.
14. Matsui Y, Egawa S, Tsukayama C, Terai A, Kuwao S, Baba S et al. Artificial neural network analysis for predicting pathological stage of clinically localized prostate cancer in the Japanese population. Jpn J Clin Oncol 2002;32:530-5.
15. Watanabe H. New concept of BPH: PCAR theory. Prostate 1998:37: 116-25.
16. Bauer JJ, Zeng J, Zhang W, McLeod DG, Sesterhenn IA, Connelly RR et al. Lateral biopsies added to the traditional sextant prostate biopsy pattern increases the detection rate of prostate cancer. Prostate Cancer Prostatic Dis 2000;3:43-6.
17. Moody J, Utans J. Architecture selection strategies for neural networks: application to corporate bond rating predictions. In: Refenes A-P, editor. Neural Networks in the Capital Markets, 2nd edn. Chichester. UK: John Wiley & Sons, 1995;287-9.
18. Finne P, Finne R, Stenman UH. Neural network analysis of clinicopathological factors in urological disease: a critical evaluation of available techniques. BJU Int 2001;88:825-31.
19. Zernikow B, Holtmannspoetter K, Michel E, Theilhaber M, Pielemeier W, Hennecke KH. Artificial neural network for predicting intracranial haemorrhage in preterm neonates. Acta Paediatr 1998;87: 969-75.
20. Dybowsld R, Weller P, Chang R and Gant V. Prediction of outcome in critically ill patients using artificial neural network synthesized by genetic algorithm. Lancet 1996;347:1146-50.
21. Schwarzer G, Vach W and Schumacher M. On the misuse of artificial neural networks for prognostic and diagnostic classification in oncology. Stat Med 2000;19:541-61.
22. Freeman RV, Eagle KA, Bates ER, Werns SW, Kline-Rogers E, Karavite D et al. Comparison of artificial neural networks with logistic regression in prediction of in-hospital death after percutaneous transluminal coronary angioplasty. Am Heart J 2000; 140:511-20.
23. Duh MS, Walker AM, Pagano M, Kronlund K. Prediction and cross-validation of neural networks versus logistic regression: using hepatic disorders as an example. Am J Epidemiol 1998; 147: 407-13.
24. Stephan C, Cammann H, Semjonow A, Diamandis EP, Wymenga LF, Lein M et al. Multicenter evaluation of an artificial neural network to increase the prostate cancer detection rate and reduce unnecessary biopsies. Clin Chem 2002;48:1279-87.
25. Djavan B, Remzi M, Zlotta A, Seitz C, Snow P, Marberger M. Novel artificial neural network for early detection of prostate cancer. J Clin Oncol 2002;20:921-9.
26. Babaian RJ, Fritsche H, Ayala A, Bhadkamkar V, Johnston DA, Naccarato W et al. Performance of a neural network in detecting prostate cancer in the prostate-specific antigen reflex range of 2.5 to 4.0 ng/ml. Urology 2000;56:1000-6.
27. Finne P, Finne R, Auvinen A, Juusela H, Aro J, Maattanen L et al. Predicting the outcome of prostate biopsy in screen-positive men by a multilayer perceptron network. Urology 2000:56:418-22.