M Han1, P B Snow, J M Brandt, A W Partin. 1. James Buchanan Brady Urological Institute, Department of Urology, Johns Hopkins Hospital, Baltimore, Maryland 21287, USA. misop@jhmi.edu
Abstract
BACKGROUND: Currently, the standard for predicting pathologic stage from information available at the time of prostate biopsy is the "Partin nomograms" that were derived using logistic regression analysis. The authors retrospectively reviewed a large series of men with clinically localized prostate carcinoma who underwent staging pelvic lymphadenectomy and radical retropubic prostatectomy. They then utilized pathologic and clinical data at the time of prostate biopsy to develop and test an artificial neural network (ANN) to predict the final pathologic stage for this group of men. They then compared the results of ANN with the previous nomograms. METHODS: Five thousand seven hundred forty-four men were treated at the authors' institution from 1985 to 1998. An ANN was developed using two randomly selected training and validation sets for predicting pathologic stage. Input variables included age, preoperative serum prostate specific antigen level, clinical TNM (tumor, lymph node, and metastasis) classification, and Gleason score from the biopsy specimen. Outcomes included organ confinement and lymph node involvement status. RESULTS: The ANN was slightly superior to the nomograms in predicting pathologic stage, such as organ confinement and lymph node involvement status. CONCLUSIONS: In predicting organ confinement and lymph node involvement status, ANN was more accurate and had a larger area under ROC than the nomograms based on the logistic regression method. Artificial neural network models can be developed and used to better predict final pathologic stage when preoperative pathologic and clinical features are known. Copyright 2001 American Cancer Society.
BACKGROUND: Currently, the standard for predicting pathologic stage from information available at the time of prostate biopsy is the "Partin nomograms" that were derived using logistic regression analysis. The authors retrospectively reviewed a large series of men with clinically localized prostate carcinoma who underwent staging pelvic lymphadenectomy and radical retropubic prostatectomy. They then utilized pathologic and clinical data at the time of prostate biopsy to develop and test an artificial neural network (ANN) to predict the final pathologic stage for this group of men. They then compared the results of ANN with the previous nomograms. METHODS: Five thousand seven hundred forty-four men were treated at the authors' institution from 1985 to 1998. An ANN was developed using two randomly selected training and validation sets for predicting pathologic stage. Input variables included age, preoperative serum prostate specific antigen level, clinical TNM (tumor, lymph node, and metastasis) classification, and Gleason score from the biopsy specimen. Outcomes included organ confinement and lymph node involvement status. RESULTS: The ANN was slightly superior to the nomograms in predicting pathologic stage, such as organ confinement and lymph node involvement status. CONCLUSIONS: In predicting organ confinement and lymph node involvement status, ANN was more accurate and had a larger area under ROC than the nomograms based on the logistic regression method. Artificial neural network models can be developed and used to better predict final pathologic stage when preoperative pathologic and clinical features are known. Copyright 2001 American Cancer Society.
Authors: Iain A McCowan; Darren C Moore; Anthony N Nguyen; Rayleen V Bowman; Belinda E Clarke; Edwina E Duhig; Mary-Jane Fry Journal: J Am Med Inform Assoc Date: 2007-08-21 Impact factor: 4.497