Predicting prostate biopsy outcome: Artificial neural networks and polychotomous regression are equivalent models

International Urology and Nephrology

Volumn 43, Issue 1, 2011, Pages 23-30

  Lawrentschuk, Nathan ^a,d   Lockwood, Gina ^b   Davies, Peter ^a   Evans, Andy ^c   Sweet, Joan ^c   Toi, Ants ^c   Fleshner, Neil E ^a  

^a UNIVERSITY OF TORONTO   (Canada)

^b Canadian Partnership Against Cancer   (Canada)

^c UNIVERSITY HEALTH NETWORK   (Canada)

^d PRINCESS MARGARET HOSPITAL   (Canada)

Author keywords

Biopsy; Logistic models; Neural network; Prostate cancer; Statistics; Treatment outcome

Indexed keywords

DUTASTERIDE; PROSTATE SPECIFIC ANTIGEN;

ADULT; AGED; ARTICLE; ARTIFICIAL NEURAL NETWORK; CANCER DIAGNOSIS; COHORT ANALYSIS; DIGITAL RECTAL EXAMINATION; HUMAN; HUMAN TISSUE; MAJOR CLINICAL STUDY; MALE; ORGAN SIZE; PROSTATE BIOPSY; PROSTATE CANCER; PROSTATIC INTRAEPITHELIAL NEOPLASIA; RETROSPECTIVE STUDY; RISK ASSESSMENT; TRANSRECTAL ULTRASONOGRAPHY;

ADULT; AGE DISTRIBUTION; AGE FACTORS; AGED; AGED, 80 AND OVER; BIOPSY; ENDOSONOGRAPHY; FOLLOW-UP STUDIES; HUMANS; INCIDENCE; MALE; MIDDLE AGED; NEOPLASM STAGING; NEURAL NETWORKS (COMPUTER); ONTARIO; PREDICTIVE VALUE OF TESTS; PROSTATE; PROSTATIC NEOPLASMS; RETROSPECTIVE STUDIES; RISK ASSESSMENT;

EID: 79953815257     PISSN: 03011623     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11255-010-9750-7     Document Type: Article

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