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Clin Med Insights Oncol. 2015 Feb 25;9:15-24. doi: 10.4137/CMO.S19739. eCollection 2015.

Development of Interpretable Predictive Models for BPH and Prostate Cancer.

Author information

1
Escuela Superior de Ingeniería Informática, Universidad de Castilla-La Mancha, Albacete, Spain.
2
Gerencia de Atención Primaria, Hospital Universitario de Albacete, Albacete, Spain.
3
Departamento de Cirugía, Hospital de la Paz, Madrid, Spain.

Abstract

BACKGROUND:

Traditional methods for deciding whether to recommend a patient for a prostate biopsy are based on cut-off levels of stand-alone markers such as prostate-specific antigen (PSA) or any of its derivatives. However, in the last decade we have seen the increasing use of predictive models that combine, in a non-linear manner, several predictives that are better able to predict prostate cancer (PC), but these fail to help the clinician to distinguish between PC and benign prostate hyperplasia (BPH) patients. We construct two new models that are capable of predicting both PC and BPH.

METHODS:

An observational study was performed on 150 patients with PSA ≥3 ng/mL and age >50 years. We built a decision tree and a logistic regression model, validated with the leave-one-out methodology, in order to predict PC or BPH, or reject both.

RESULTS:

Statistical dependence with PC and BPH was found for prostate volume (P-value < 0.001), PSA (P-value < 0.001), international prostate symptom score (IPSS; P-value < 0.001), digital rectal examination (DRE; P-value < 0.001), age (P-value < 0.002), antecedents (P-value < 0.006), and meat consumption (P-value < 0.08). The two predictive models that were constructed selected a subset of these, namely, volume, PSA, DRE, and IPSS, obtaining an area under the ROC curve (AUC) between 72% and 80% for both PC and BPH prediction.

CONCLUSION:

PSA and volume together help to build predictive models that accurately distinguish among PC, BPH, and patients without any of these pathologies. Our decision tree and logistic regression models outperform the AUC obtained in the compared studies. Using these models as decision support, the number of unnecessary biopsies might be significantly reduced.

KEYWORDS:

benign prostate hyperplasia; observational study; primary care; prostate biopsy; prostate cancer; prostate pathology prediction

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