Table 1.
Examples for multivariate models using %fPSA for diagnosis of PCa (1998-2004)
| First Author [Ref.] (n of pts.; % of PCa) |
Year | Screening | Model (ranking) | PSA assays (company) | tPSA range (ng/ml) | contributing factors (if numbered, by value) | AUC | Specificity at 95% sensitivity |
|---|---|---|---|---|---|---|---|---|
|
Carlson (n=3773; 33% PCa) |
1998 1999 | no yes | LR | Tosoh (Dianon) | 4-20 | l.%fPSA, 2.age 3.tPSA | n.a. | 34 (LR) 23(%fPSA) |
|
Virtanen (n=212; 25% PCa) |
1999 | yes | l.LR 2. ANN |
ProStatus (Wallac) | 3-10 (3-45) |
l.%fPSA 2.DRE 3.heredity |
0.81 (LR for tPSA3-45) %fPSAn.a. | n.a. |
|
Finne [58] (n=656; 23% PCa) |
2000 | yes | 1.ANN 2.LR |
ProStatus (Wallac) | 4-10 | l.%fPSA 2.volume 3.DRE4.tPSA |
n.a. | 33 (ANN) 24 (LR) 19 (%f PSA) |
|
Babaian (n=151; 25% PCa) |
2000 | yes | ANN | Tandem R (Beckman Coulter) |
2.5-4 | %fPSA, tPSA, age, PAP, CK | 0.74 ANN (0.64%fPSA) |
51 (ANN) 39 (PSAD) 10(%fPSA) |
|
Horninger (n=3474; n.a.) |
2001 | yes | ANN LR |
Abbot IMX (Abbott) |
n.a. PSA>4 or DRE+ |
age, tPSA, %f PSA, DRE, volume, PSAD, PSAD-TZ, TZ-volume | n.a. |
~27 (ANN) ~13 (%f PSA) ~13(tPSA) |
|
Stephan (n=1188; 61% PCa) |
2002 | no | ANN LR |
IMMULITE (Bayer) |
2-20 | 1.DRE 2.%f PSA 3.volume 4.tPSA5.age |
0.86 (ANN) 0.75 (%f PSA) |
43 (ANN) 26 (%f PSA) |
|
Remzi (n=820; 10% PCa) |
2003 | no | ANN, LR | AxSYM (Abbott) |
4-10 | tPSA,%f PSA, volume, PSAD, PSAD-TZ, TZ-volume | 0.83 (ANN) 0.79 (LR) 0.745 (%f PSA) |
68 (ANN) 54 (LR) 33.5 (%f PSA) |
|
Finne (n=1775; 22% PCa) |
2004 | yes | 1. LR 2.ANN |
ProStatus (Wallac) | 4-10 | 1.DRE 2.%f PSA 3.volume 4.tPSA | 0.764 (LR) 0.760 (ANN) 0.718 (%f PSA) |
22 (LR) 19 (ANN) 17(%f PSA) |
|
Sokoll [70] (n=566; 43% PCa) |
2010 | not available | 0.79 (LR model) | 80 | 45 |
Abbreviations: AUC: area under the (ROC) curve; n.a.: not available; LR: logistic regression; ANN: artificial neural network, PAP: prostate alkaline phosphatase, CK: creatinkinase; PSAD: PSA density, PSAD-TZ: transition zone density; DRE: digital rectal examination