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Classification models for early detection of prostate cancer.

Wichard JD, Cammann H, Stephan C, Tolxdorff T - J. Biomed. Biotechnol. (2008)

Bottom Line: We build ensembles of classification models in order to increase the classification performance.We measure the performance of our models in an extensive cross-validation procedure and compare different classification models.The datasets come from clinical examinations and some of the classification models are already in use to support the urologists in their clinical work.

View Article: PubMed Central - PubMed

Affiliation: Institute of Medical Informatics, Charité - Universitätsmedizin, Hindenburgdamm 30, 12200 Berlin, Germany. joergwichard@web.de

ABSTRACT
We investigate the performance of different classification models and their ability to recognize prostate cancer in an early stage. We build ensembles of classification models in order to increase the classification performance. We measure the performance of our models in an extensive cross-validation procedure and compare different classification models. The datasets come from clinical examinations and some of the classification models are already in use to support the urologists in their clinical work.

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Related in: MedlinePlus

A scatterplot matrix of the data. Each box shows a pair of variables and the cases arecolor-coded, a red cross marks PCa, and a blue circle non-PCa. The DRE is a binary variable (suspicious or nonsuspicious).
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Related In: Results  -  Collection


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fig1: A scatterplot matrix of the data. Each box shows a pair of variables and the cases arecolor-coded, a red cross marks PCa, and a blue circle non-PCa. The DRE is a binary variable (suspicious or nonsuspicious).

Mentions: We had access to the clinically available data of 506 patients with 313 cases of prostatecancer (PCa) and 193 non-PCa. The data were selected from a group of 780patients randomly. The data entry for each patient included age, PSA, the ratioof free to total prostate-specific antigen (PSA-Ratio), TRUS, and thediagnostic finding from the DRE which was a binary variable (suspicious ornonsuspicious). Blood sampling and handling were performed as described inStephan et al. [11].The samples were taken before any diagnostic or therapeutic procedures, andsera were stored at 80°C until analyzed.After thawing at room temperature, samples were processed within 3 hours.Prostate volume was determined by transrectal ultrasound using the prolateellipse formula. The scatter plot of the variables under investigation is shownin Figure 1. PCa and non-PCa patients were histologically confirmed by 6–8core prostate biopsy.


Classification models for early detection of prostate cancer.

Wichard JD, Cammann H, Stephan C, Tolxdorff T - J. Biomed. Biotechnol. (2008)

A scatterplot matrix of the data. Each box shows a pair of variables and the cases arecolor-coded, a red cross marks PCa, and a blue circle non-PCa. The DRE is a binary variable (suspicious or nonsuspicious).
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2366047&req=5

fig1: A scatterplot matrix of the data. Each box shows a pair of variables and the cases arecolor-coded, a red cross marks PCa, and a blue circle non-PCa. The DRE is a binary variable (suspicious or nonsuspicious).
Mentions: We had access to the clinically available data of 506 patients with 313 cases of prostatecancer (PCa) and 193 non-PCa. The data were selected from a group of 780patients randomly. The data entry for each patient included age, PSA, the ratioof free to total prostate-specific antigen (PSA-Ratio), TRUS, and thediagnostic finding from the DRE which was a binary variable (suspicious ornonsuspicious). Blood sampling and handling were performed as described inStephan et al. [11].The samples were taken before any diagnostic or therapeutic procedures, andsera were stored at 80°C until analyzed.After thawing at room temperature, samples were processed within 3 hours.Prostate volume was determined by transrectal ultrasound using the prolateellipse formula. The scatter plot of the variables under investigation is shownin Figure 1. PCa and non-PCa patients were histologically confirmed by 6–8core prostate biopsy.

Bottom Line: We build ensembles of classification models in order to increase the classification performance.We measure the performance of our models in an extensive cross-validation procedure and compare different classification models.The datasets come from clinical examinations and some of the classification models are already in use to support the urologists in their clinical work.

View Article: PubMed Central - PubMed

Affiliation: Institute of Medical Informatics, Charité - Universitätsmedizin, Hindenburgdamm 30, 12200 Berlin, Germany. joergwichard@web.de

ABSTRACT
We investigate the performance of different classification models and their ability to recognize prostate cancer in an early stage. We build ensembles of classification models in order to increase the classification performance. We measure the performance of our models in an extensive cross-validation procedure and compare different classification models. The datasets come from clinical examinations and some of the classification models are already in use to support the urologists in their clinical work.

Show MeSH
Related in: MedlinePlus