Prostate specific antigen and acinar density: a new dimension, the “Prostatocrit”
ABSTRACT Background Prostate-specific antigen densities have limited success in diagnosing prostate cancer. We emphasise the importance of the peripheral zone when considered with its cellular constituents, the “prostatocrit”. Objective Using zonal volumes and asymmetry of glandular acini, we ge...
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| Format: | Article |
| Language: | English |
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Sociedade Brasileira de Urologia
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| Series: | International Brazilian Journal of Urology |
| Subjects: | |
| Online Access: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1677-55382017000200230&lng=en&tlng=en |
| _version_ | 1811304378561200128 |
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| author | Simon Robinson Marc Laniado Bruce Montgomery |
| author_facet | Simon Robinson Marc Laniado Bruce Montgomery |
| author_sort | Simon Robinson |
| collection | DOAJ |
| description | ABSTRACT Background Prostate-specific antigen densities have limited success in diagnosing prostate cancer. We emphasise the importance of the peripheral zone when considered with its cellular constituents, the “prostatocrit”. Objective Using zonal volumes and asymmetry of glandular acini, we generate a peripheral zone acinar volume and density. With the ratio to the whole gland, we can better predict high grade and all grade cancer. We can model the gland into its acinar and stromal elements. This new “prostatocrit” model could offer more accurate nomograms for biopsy. Materials and Methods 674 patients underwent TRUS and biopsy. Whole gland and zonal volumes were recorded. We compared ratio and acinar volumes when added to a “clinic” model using traditional PSA density. Univariate logistic regression was used to find significant predictors for all and high grade cancer. Backwards multiple logistic regression was used to generate ROC curves comparing the new model to conventional density and PSA alone. Outcome and results Prediction of all grades of prostate cancer: significant variables revealed four significant “prostatocrit” parameters: log peripheral zone acinar density; peripheral zone acinar volume/whole gland acinar volume; peripheral zone acinar density/whole gland volume; peripheral zone acinar density. Acinar model (AUC 0.774), clinic model (AUC 0.745) (P=0.0105). Prediction of high grade prostate cancer: peripheral zone acinar density (“prostatocrit”) was the only significant density predictor. Acinar model (AUC 0.811), clinic model (AUC 0.769) (P=0.0005). Conclusion There is renewed use for ratio and “prostatocrit” density of the peripheral zone in predicting cancer. This outperforms all traditional density measurements. |
| first_indexed | 2024-04-13T08:04:52Z |
| format | Article |
| id | doaj.art-98bc734da77b44cea75bac8c0c1e8144 |
| institution | Directory Open Access Journal |
| issn | 1677-6119 |
| language | English |
| last_indexed | 2024-04-13T08:04:52Z |
| publisher | Sociedade Brasileira de Urologia |
| record_format | Article |
| series | International Brazilian Journal of Urology |
| spelling | doaj.art-98bc734da77b44cea75bac8c0c1e81442022-12-22T02:55:11ZengSociedade Brasileira de UrologiaInternational Brazilian Journal of Urology1677-611943223023810.1590/s1677-5538.ibju.2016.0145S1677-55382017000200230Prostate specific antigen and acinar density: a new dimension, the “Prostatocrit”Simon RobinsonMarc LaniadoBruce MontgomeryABSTRACT Background Prostate-specific antigen densities have limited success in diagnosing prostate cancer. We emphasise the importance of the peripheral zone when considered with its cellular constituents, the “prostatocrit”. Objective Using zonal volumes and asymmetry of glandular acini, we generate a peripheral zone acinar volume and density. With the ratio to the whole gland, we can better predict high grade and all grade cancer. We can model the gland into its acinar and stromal elements. This new “prostatocrit” model could offer more accurate nomograms for biopsy. Materials and Methods 674 patients underwent TRUS and biopsy. Whole gland and zonal volumes were recorded. We compared ratio and acinar volumes when added to a “clinic” model using traditional PSA density. Univariate logistic regression was used to find significant predictors for all and high grade cancer. Backwards multiple logistic regression was used to generate ROC curves comparing the new model to conventional density and PSA alone. Outcome and results Prediction of all grades of prostate cancer: significant variables revealed four significant “prostatocrit” parameters: log peripheral zone acinar density; peripheral zone acinar volume/whole gland acinar volume; peripheral zone acinar density/whole gland volume; peripheral zone acinar density. Acinar model (AUC 0.774), clinic model (AUC 0.745) (P=0.0105). Prediction of high grade prostate cancer: peripheral zone acinar density (“prostatocrit”) was the only significant density predictor. Acinar model (AUC 0.811), clinic model (AUC 0.769) (P=0.0005). Conclusion There is renewed use for ratio and “prostatocrit” density of the peripheral zone in predicting cancer. This outperforms all traditional density measurements.http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1677-55382017000200230&lng=en&tlng=enAcinar CellsProstatic NeoplasmsDiagnosisPSA |
| spellingShingle | Simon Robinson Marc Laniado Bruce Montgomery Prostate specific antigen and acinar density: a new dimension, the “Prostatocrit” International Brazilian Journal of Urology Acinar Cells Prostatic Neoplasms Diagnosis PSA |
| title | Prostate specific antigen and acinar density: a new dimension, the “Prostatocrit” |
| title_full | Prostate specific antigen and acinar density: a new dimension, the “Prostatocrit” |
| title_fullStr | Prostate specific antigen and acinar density: a new dimension, the “Prostatocrit” |
| title_full_unstemmed | Prostate specific antigen and acinar density: a new dimension, the “Prostatocrit” |
| title_short | Prostate specific antigen and acinar density: a new dimension, the “Prostatocrit” |
| title_sort | prostate specific antigen and acinar density a new dimension the prostatocrit |
| topic | Acinar Cells Prostatic Neoplasms Diagnosis PSA |
| url | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1677-55382017000200230&lng=en&tlng=en |
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