title: Combining artificial neural networks and transrectal ultrasound in the diagnosis of prostate cancer.
abstract: Arguably the most important step in the prognosis of prostate cancer is early diagnosis. More than 1 million transrectal ultrasound (TRUS)-guided prostate needle biopsies are performed annually in the United States, resulting in the detection of 200,000 new cases per year. Unfortunately, the urologist's ability to diagnose prostate cancer has not kept pace with therapeutic advances; currently, many men are facing the need for prostate biopsy with the likelihood that the result will be inconclusive. This paper will focus on the tools available to assist the clinician in predicting the outcome of the prostate needle biopsy. We will examine the use of "machine learning" models (artificial intelligence), in the form of artificial neural networks (ANNs), to predict prostate biopsy outcomes using prebiopsy variables. Currently, six validated predictive models are available. Of these, five are machine learning models, and one is based on logistic regression. The role of ANNs in providing valuable predictive models to be used in conjunction with TRUS appears promising. In the few studies that have compared machine learning to traditional statistical methods, ANN and logistic regression appear to function equivalently when predicting biopsy outcome. With the introduction of more complex prebiopsy variables, ANNs are in a commanding position for use in predictive models. Easy and immediate physician access to these models will be imperative if their full potential is to be realized.
authors: Section of Urology and Renal Transplantation, Virginia Mason Medical Center, Seattle, Washington, USA.
authors: Christopher R
authors: CR
authors: Porter
authors: E David
authors: ED
authors: Crawford
mesh_terms: D001706:Biopsy; D003937:Diagnosis, Differential; D006801:Humans; D008297:Male; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D011379:Prognosis; D011471:Prostatic Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D012044:Regression Analysis; D014463:Ultrasonography