Improved Detection of Prostate Cancer Using Classification and Regression Tree Analysis

Garzotto, Mark; Beer, Tomasz M.; Hudson, Rebekah; Peters, Laura; Hsieh, Yu‐Chia; Barrera, E Alejandro; Klein, Thomas; Mori, Motomi

doi:10.1200/jco.2005.11.136

Cited by 105 publications

(73 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Prostate volume has previously been incorporated into predictive tools. 30 Prostate volume is, however, usually measured when the patient is subjected to TRUS-guided biopsies, and seldom measured in populations subjected to screening. Hence, it would appear logical and intuitive that the ideal prediction tool should use only data generally available (e.g.…”

Section: Discussionmentioning

confidence: 99%

Free PSA isoforms and intact and cleaved forms of urokinase plasminogen activator receptor in serum improve selection of patients for prostate cancer biopsy

Steuber

Vickers

Haese

et al. 2007

Intl Journal of Cancer

View full text Add to dashboard Cite

Clinicians currently use simple cut-points, such as serum prostatespecific antigen (PSA) 4 ng/ml, to decide whether to recommend further work-up for prostate cancer (PCa). As an alternative strategy, we evaluated multivariable models giving probabilities of a PCa diagnosis based on PSA and several circulating novel biomarkers. We measured total PSA, free PSA (fPSA), fPSA subfractions (single-chain fPSA-I and multichain fPSA-N), total human glandular kallikrein 2 (hK2) and full-length and cleaved forms of soluble urokinase plasminogen activator receptor (suPAR) in pretreatment serum from 355 men referred for prostate biopsy. Age and total PSA were combined in a ''base'' regression model to predict biopsy outcome. We then compared this base model to models supplemented by various combinations of circulating markers, using concordance index (AUC) to measure diagnostic discrimination. PCa prediction was significantly enhanced by models supplemented by measurements of suPAR fragments and fPSA isoforms. Addition of these markers improved bootstrap-corrected AUC from 0.611 for a cut-point and 0.706 for the base model to 0.754 for the full model (p 5 0.005). This improved diagnostic accuracy was also seen in subanalysis of patients with PSA 2-9.99 ng/ml and normal findings on DRE (0.652 vs. 0.715, p 5 0.039). In this setting, hK2 did not add diagnostic information. Measurements of individual forms of suPAR and PSA isoforms contributed significantly to discrimination of men with PCa from those with no evidence of malignancy. ' 2006 Wiley-Liss, Inc.

show abstract

Section: Discussionmentioning

confidence: 99%

Free PSA isoforms and intact and cleaved forms of urokinase plasminogen activator receptor in serum improve selection of patients for prostate cancer biopsy

Steuber

Vickers

Haese

et al. 2007

Intl Journal of Cancer

View full text Add to dashboard Cite

show abstract

“…In the last ten years, CART has been used extensively in many different disciplines as a prediction methodology. For instance, it has been used in meteorology to predict UV Radiation on the ground (Burrows 1996), in engineering to predict the quality of glass coating (Li et al 2003), in economics to predict views in welfare policy (Keely and Tan 2005), in neurology to predict the recovery of memory after brain injury (Stuss et al 2000), in computer science to predict storage device performance (Wang et al 2004), and very recently in medical science to predict the occurrence of prostate cancer (Garzotto et al 2005).…”

Section: Statistical Learning Techniquesmentioning

confidence: 99%

From Story Line to Box Office: A New Approach for Green-Lighting Movie Scripts

2007

View full text Add to dashboard Cite

Movie studios often have to choose among thousands of scripts to decide which ones to turn into movies. Despite the huge amount of money at stake, this process-known as green-lightingin the movie industry-is largely a guesswork based on experts' experience and intuitions. In this paper, we propose a new approach to help studios evaluate scripts that will then lead to more profitable green-lighting decisions. Our approach combines screenwriting domain knowledge, natural-language processing techniques, and statistical learning methods to forecast a movie's return on investment (ROI) based only on textual information available in movie scripts. We test our model in a holdout decision task to show that our model is able to significantly improve a studio's gross ROI. 1 We thank the AE and the three anonymous reviewers for their valuable and insightful comments on our manuscript. We are, of course, responsible for the contents of this paper.2 From Storyline to Box Office: A New Approach for Green-Lighting Movie Scripts AbstractMovie studios often have to choose among thousands of scripts to decide which ones to turn into movies. Despite the huge amount of money at stake, this process, known as "green-lighting" in the movie industry, is largely a guesswork based on experts' experience and intuitions. In this paper, we propose a new approach to help studios evaluate scripts which will then lead to more profitable green-lighting decisions. Our approach combines screenwriting domain knowledge, natural language processing techniques, and statistical learning methods to forecast a movie's return-on-investment based only on textual information available in movie scripts. We test our model in a holdout decision task to show that our model is able to improve a studio's gross return-on-investment significantly.

show abstract

“…Decision tree analysis is a core component of data mining and predictive modeling [12], and it is utilized by decision makers in various fields of business. Recent publications on decision tree analysis indicate its usefulness for defining prognostic factors in various diseases such as prostate cancer [13], diabetes [14], melanoma [15,16], colorectal carcinoma [17,18], and liver failure [19]. The results of the analysis are presented as a tree structure, which is intuitive and facilitates the allocation of patients into subgroups by following the flow chart form [20].…”

Section: Introductionmentioning

confidence: 99%

Pretreatment prediction of response to peginterferon plus ribavirin therapy in genotype 1 chronic hepatitis C using data mining analysis

et al. 2010

View full text Add to dashboard Cite

show abstract

Improved Detection of Prostate Cancer Using Classification and Regression Tree Analysis

Abstract: Application of CART analysis to the prostate biopsy decision results in a significant reduction in unnecessary biopsies while retaining a high degree of sensitivity when compared with the standard of performing a biopsy of all patients with an abnormal PSA or DRE.

Cited by 105 publications

References 32 publications

Free PSA isoforms and intact and cleaved forms of urokinase plasminogen activator receptor in serum improve selection of patients for prostate cancer biopsy

Free PSA isoforms and intact and cleaved forms of urokinase plasminogen activator receptor in serum improve selection of patients for prostate cancer biopsy

From Story Line to Box Office: A New Approach for Green-Lighting Movie Scripts

Pretreatment prediction of response to peginterferon plus ribavirin therapy in genotype 1 chronic hepatitis C using data mining analysis

Contact Info

Product

Resources

About