Initial Biopsy Outcome Prediction-Head-to-Head Comparison of a Logistic Regression-Based Nomogram versus Artificial Neural Network

European Urology

Volumn 51, Issue 5, 2007, Pages 1236-1243

  Chun, Felix K H ^a,b   Graefen, Markus ^b   Briganti, Alberto ^a   Gallina, Andrea ^a   Hopp, Julia ^b   Kattan, Michael W ^c   Huland, Hartwig ^b   Karakiewicz, Pierre I ^a  

^a UNIVERSITÉ DE MONTRÉAL   (Canada)

^b UNIVERSITY OF HAMBURG   (Germany)

^c LERNER RESEARCH INSTITUTE   (United States)

Author keywords

Artificial neural network; External validation; Head to head comparison; Initial prostate biopsy; Nomogram; Prostate cancer

Indexed keywords

PROSTATE SPECIFIC ANTIGEN;

ACCURACY; ADULT; AGE; AGED; ARTICLE; ARTIFICIAL NEURAL NETWORK; BENIGN TUMOR; COHORT ANALYSIS; COMPARATIVE STUDY; DIGITAL RECTAL EXAMINATION; HUMAN; LOGISTIC REGRESSION ANALYSIS; MAJOR CLINICAL STUDY; MALE; NOMOGRAM; PREDICTIVE VALIDITY; PRIORITY JOURNAL; PROBABILITY; PROSTATE BIOPSY; PROSTATE CANCER; PROSTATE TUMOR; TUMOR VOLUME;

ADULT; AGED; BIOPSY, NEEDLE; HUMANS; LOGISTIC MODELS; MALE; MIDDLE AGED; MODELS, STATISTICAL; NEURAL NETWORKS (COMPUTER); NOMOGRAMS; PREDICTIVE VALUE OF TESTS; PROSTATE; PROSTATIC NEOPLASMS; SENSITIVITY AND SPECIFICITY;

EID: 33947238244     PISSN: 03022838     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eururo.2006.07.021     Document Type: Article

References (15)

1. Djavan B., Remzi M., Zlotta A., Seitz C., Snow P., and Marberger M. Novel artificial neural network for early detection of prostate cancer. J Clin Oncol 20 (2002) 921-929
2. Stephan C., Cammann H., Semjonow A., et al. Multicenter evaluation of an artificial neural network to increase the prostate cancer detection rate and reduce unnecessary biopsies. Clin Chem 48 (2002) 1279-1287
3. Garzotto M., Hudson R.G., Peters L., et al. Predictive modeling for the presence of prostate carcinoma using clinical, laboratory, and ultrasound parameters in patients with prostate specific antigen levels ≤10 ng/mL. Cancer 98 (2003) 1417-1422
4. Karakiewicz P.I., Benayoun S., Kattan M.W., et al. Development and validation of a nomogram predicting the outcome of prostate biopsy based on patient age, digital rectal examination and serum prostate specific antigen. J Urol 173 (2005) 1930-1934
5. Eastham J.A., May R., Robertson J.L., Sartor O., and Kattan M.W. Development of a nomogram that predicts the probability of a positive prostate biopsy in men with an abnormal digital rectal examination and a prostate-specific antigen between 0 and 4 ng/mL. Urology 54 (1999) 709-712
6. In: Greene F., Page D., Fleming I., et al. (Eds). American Joint Committee on Cancer staging manual. 6th ed. (2002), Springer, New York
7. Gleason D.F. Histologic grading of prostate cancer: a perspective. Hum Pathol 23 (1992) 273-279
8. Lachenbruch P.A. Some misuses of discriminant analysis. Methods Inf Med 16 (1977) 255-258
9. Schwarzer G., Vach W., and Schumacher M. On the misuses of artificial neural networks for prognostic and diagnostic classification in oncology. Stat Med 19 (2000) 541-561
10. Ficarra V., Novella G., Novara G., et al. The potential impact of prostate volume in the planning of optimal number of cores in the systematic transperineal prostate biopsy. Eur Urol 48 (2005) 932-937
11. Karakiewicz P.I., Bazinet M., Aprikian A.G., et al. Outcome of sextant biopsy according to gland volume. Urology 49 (1997) 55-59
12. Vashi A.R., Wojno K.J., Gillespie B., and Oesterling J.E. A model for the number of cores per prostate biopsy based on patient age and prostate gland volume. J Urol 159 (1998) 920-924
13. Aus G., Abbou C.C., Bolla M., Heidenreich A., Schmid H.P., van Poppel H., et al. EAU guidelines on prostate cancer. Eur Urol 48 (2005) 546-551
14. Bostwick D.G., and Meiers I. Prostate biopsy and optimization of cancer yield. Eur Urol 49 (2006) 415-417
15. Descazeaud A., Rubin M.A., Allory Y., et al. What information are urologists extracting from prostate needle biopsy reports and what do they need for clinical management of prostate cancer?. Eur Urol 48 (2005) 911-915