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Artificial neural network (ANN) velocity better identifies benign prostatic hyperplasia but not prostate cancer compared with PSA velocity.

Stephan C, Büker N, Cammann H, Meyer HA, Lein M, Jung K - BMC Urol (2008)

Bottom Line: However, BPH patients benefit from ANNV since the stable values are significantly more (83% vs. 65%) and increasing values are less frequently (11% vs. 21%) if the ANNV is used instead of the PSAV.PSAV has only limited usefulness for the detection of PCa with only 71% increasing PSA values, while 29% of all PCa do not have the typical PSAV.The ANNV cannot improve the PCa detection rate but may save 11-17% of unnecessary prostate biopsies in known BPH patients.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Urology, Charité - Universitätsmedizin Berlin, Germany. carsten.stephan@charite.de

ABSTRACT

Background: To validate an artificial neural network (ANN) based on the combination of PSA velocity (PSAV) with a %free PSA-based ANN to enhance the discrimination between prostate cancer (PCa) and benign prostate hyperplasia (BPH).

Methods: The study comprised 199 patients with PCa (n = 49) or BPH (n = 150) with at least three PSA estimations and a minimum of three months intervals between the measurements. Patients were classified into three categories according to PSAV and ANN velocity (ANNV) calculated with the %free based ANN "ProstataClass". Group 1 includes the increasing PSA and ANN values, Group 2 the stable values, and Group 3 the decreasing values.

Results: 71% of PCa patients typically have an increasing PSAV. In comparison, the ANNV only shows this in 45% of all PCa patients. However, BPH patients benefit from ANNV since the stable values are significantly more (83% vs. 65%) and increasing values are less frequently (11% vs. 21%) if the ANNV is used instead of the PSAV.

Conclusion: PSAV has only limited usefulness for the detection of PCa with only 71% increasing PSA values, while 29% of all PCa do not have the typical PSAV. The ANNV cannot improve the PCa detection rate but may save 11-17% of unnecessary prostate biopsies in known BPH patients.

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Related in: MedlinePlus

ROC curves for tPSA (green, AUC 0.5), %fPSA (blue, AUC 0.64), PSAD (black, AUC 0.69) and ANNV (red, AUC 0.57) to show the different behavior of the curve regardless of the AUC at tPSA 4–10 ng/mL (PSAV and ANN not shown, given in table 4).
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Figure 1: ROC curves for tPSA (green, AUC 0.5), %fPSA (blue, AUC 0.64), PSAD (black, AUC 0.69) and ANNV (red, AUC 0.57) to show the different behavior of the curve regardless of the AUC at tPSA 4–10 ng/mL (PSAV and ANN not shown, given in table 4).

Mentions: Table 3 shows the ROC analysis for all 199 patients by comparing the AUC for tPSA, %fPSA, PSAD, PSAV, ANN output and the ANNV. PSAD was the best parameter to differentiate between PCa and BPH and neither ANN nor ANNV could improve this. At 95% sensitivity, PSAD performed better than all other parameters. On the other hand, at 95% specificity, the ANNV was the best available parameter with a sensitivity of 32.7% and significantly better performance compared with all others except %fPSA (P = 0.44). A similar behavior is seen for the 4–10 ng/mL tPSA range in Table 4. Again, regarding the AUC comparison and the specificities at 95% sensitivity, PSAD performed best, but did only reach significance levels to all others at 95% sensitivity but not for the AUC comparison. At 95% specificity, the ANNV (sensitivity 37.5%) demonstrated also within the tPSA range 4–10 ng/mL the ability to perform significantly better than all other parameters except the ANN output (P = 0.07). Figure 1 shows for the tPSA range 4–10 ng/mL that the ANNV has the steepest increase of the ROC curve with the highest sensitivities at 95% and 90% specificity, respectively. This may be more important for repeat biopsies, where biopsies in general should be avoided.


Artificial neural network (ANN) velocity better identifies benign prostatic hyperplasia but not prostate cancer compared with PSA velocity.

Stephan C, Büker N, Cammann H, Meyer HA, Lein M, Jung K - BMC Urol (2008)

ROC curves for tPSA (green, AUC 0.5), %fPSA (blue, AUC 0.64), PSAD (black, AUC 0.69) and ANNV (red, AUC 0.57) to show the different behavior of the curve regardless of the AUC at tPSA 4–10 ng/mL (PSAV and ANN not shown, given in table 4).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2543033&req=5

Figure 1: ROC curves for tPSA (green, AUC 0.5), %fPSA (blue, AUC 0.64), PSAD (black, AUC 0.69) and ANNV (red, AUC 0.57) to show the different behavior of the curve regardless of the AUC at tPSA 4–10 ng/mL (PSAV and ANN not shown, given in table 4).
Mentions: Table 3 shows the ROC analysis for all 199 patients by comparing the AUC for tPSA, %fPSA, PSAD, PSAV, ANN output and the ANNV. PSAD was the best parameter to differentiate between PCa and BPH and neither ANN nor ANNV could improve this. At 95% sensitivity, PSAD performed better than all other parameters. On the other hand, at 95% specificity, the ANNV was the best available parameter with a sensitivity of 32.7% and significantly better performance compared with all others except %fPSA (P = 0.44). A similar behavior is seen for the 4–10 ng/mL tPSA range in Table 4. Again, regarding the AUC comparison and the specificities at 95% sensitivity, PSAD performed best, but did only reach significance levels to all others at 95% sensitivity but not for the AUC comparison. At 95% specificity, the ANNV (sensitivity 37.5%) demonstrated also within the tPSA range 4–10 ng/mL the ability to perform significantly better than all other parameters except the ANN output (P = 0.07). Figure 1 shows for the tPSA range 4–10 ng/mL that the ANNV has the steepest increase of the ROC curve with the highest sensitivities at 95% and 90% specificity, respectively. This may be more important for repeat biopsies, where biopsies in general should be avoided.

Bottom Line: However, BPH patients benefit from ANNV since the stable values are significantly more (83% vs. 65%) and increasing values are less frequently (11% vs. 21%) if the ANNV is used instead of the PSAV.PSAV has only limited usefulness for the detection of PCa with only 71% increasing PSA values, while 29% of all PCa do not have the typical PSAV.The ANNV cannot improve the PCa detection rate but may save 11-17% of unnecessary prostate biopsies in known BPH patients.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Urology, Charité - Universitätsmedizin Berlin, Germany. carsten.stephan@charite.de

ABSTRACT

Background: To validate an artificial neural network (ANN) based on the combination of PSA velocity (PSAV) with a %free PSA-based ANN to enhance the discrimination between prostate cancer (PCa) and benign prostate hyperplasia (BPH).

Methods: The study comprised 199 patients with PCa (n = 49) or BPH (n = 150) with at least three PSA estimations and a minimum of three months intervals between the measurements. Patients were classified into three categories according to PSAV and ANN velocity (ANNV) calculated with the %free based ANN "ProstataClass". Group 1 includes the increasing PSA and ANN values, Group 2 the stable values, and Group 3 the decreasing values.

Results: 71% of PCa patients typically have an increasing PSAV. In comparison, the ANNV only shows this in 45% of all PCa patients. However, BPH patients benefit from ANNV since the stable values are significantly more (83% vs. 65%) and increasing values are less frequently (11% vs. 21%) if the ANNV is used instead of the PSAV.

Conclusion: PSAV has only limited usefulness for the detection of PCa with only 71% increasing PSA values, while 29% of all PCa do not have the typical PSAV. The ANNV cannot improve the PCa detection rate but may save 11-17% of unnecessary prostate biopsies in known BPH patients.

Show MeSH
Related in: MedlinePlus