| Literature DB >> 33425794 |
Marco Roscigno1, Armando Stabile2, Giovanni Lughezzani3, Pietro Pepe4, Lucio Dell'Atti5, Angelo Naselli6, Richard Naspro1, Maria Nicolai1, Giovanni La Croce1, Aljoulani Muhannad1, Giovanna Perugini7, Giorgio Guazzoni3,8, Francesco Montorsi2, Luca Balzarini9, Sandro Sironi7,10, Luigi F Da Pozzo1,10.
Abstract
INTRODUCTION &Entities:
Keywords: Active surveillance; MRI-TRUS fusion; Magnetic resonance imaging; Prostate biopsy; Prostate cancer
Year: 2020 PMID: 33425794 PMCID: PMC7767935 DOI: 10.1016/j.prnil.2020.05.003
Source DB: PubMed Journal: Prostate Int ISSN: 2287-8882
Basic Model: Multivariable logistic regression model predicting disease reclassification (presence of GG2 PCa); AUC: 0.69
| Predictors | Multivariable analysis | |
|---|---|---|
| OR (95% CI) | ||
| Age | 1.038 (1.005 – 1.073) | 0.025 |
| PSAD (per 0.1-unit increase) | 1.548 (1.160 – 1.751) | 0.001 |
| Number of positive cores at baseline | 1.426 (1.206 – 1.988) | 0.001 |
PSAD: prostate-specific antigen density; GG2: Grade Group 2; PCa: prostate cancer; CI: confindence interval; OR: odds ratio; AUC: area under the curve.
MRI Model: Multivariable logistic regression model predicting disease reclassification (presence of GG2 PCa); AUC: 0.64
| Predictors | Multivariable analysis | |
|---|---|---|
| OR (95% CI) | ||
| PI-RADS | ||
| 1-2 | Ref | |
| 3 | 2.539 (1.301 – 4.953) | 0.006 |
| 4 | 2.925 (1.662 – 5.150) | <0.001 |
| 5 | 5.275 (2.437 – 11.418) | <0.001 |
PI-RADS: Prostate Imaging Reporting and Data System; GG2: Grade Group 2; PCa: prostate cancer; CI: confindence interval; OR: odds ratio; AUC: area under the curve.
Full Model: Multivariable logistic regression model predicting disease reclassification (presence of GG2 PCa); AUC: 0.74
| Predictors | Multivariable analysis | |
|---|---|---|
| OR (95% CI) | ||
| Age | 1.040 (1.005 – 1.076) | 0.023 |
| PSAD (per 0.1-unit increase) | 1.324 (1.017 – 1.724) | 0.037 |
| Number of positive cores at baseline | 1.441 (1.168 – 1.778) | 0.001 |
| PI-RADS | ||
| 1-2 | Ref | |
| 3 | 2.458 (1.213 – 4.979) | 0.013 |
| 4 | 3.007 (1.643 – 5.505) | <0.001 |
| 5 | 3.898 (1.699 – 8.944) | 0.001 |
PSAD: prostate-specific antigen density; PI-RADS: Prostate Imaging Reporting and Data System; GG2: Grade Group 2; PCa: prostate cancer; CI: confindence interval; OR: odds ratio; AUC: area under the curve.
Fig. 1Clinical net benefit deriving from the use of the three evaluated models.
Fig. 2ROC curves with AUC for the three predictive models. ROC, receiver operating characteristic; AUC, area under the curve.
Patient characteristics
| Variables | Total |
|---|---|
| Age (yrs),mean (95% CI) | 66.7 (66.1 – 67.4) |
| PSA (ng/mL),mean (95% CI) | 6.50 (6.10 – 6.89) |
| Prostate volume (mL), mean (95% CI) | 53.9 (51.4 – 56.3) |
| PSAD (ng/ml/cc), mean (95% CI) | 0.12 (0.09-0.16) |
| Positive cores at baseline # (%) | |
| 1 | 226 (58.1) |
| 2 | 114 (29.3) |
| >2 | 49 (12.6) |
| Biopsy, n (%) | |
| Confirmatory | 320 (82.3) |
| Follow-up | 69 (17.7) |
| PI-RADS, n (%) | |
| 1-2 (negative) | 127 (32.6) |
| 3 | 72 (18.5) |
| 4 | 150 (38.6) |
| 5 | 40 (10.3) |
| Rebiopsy results, n (%) | |
| No upgrading | 264 (67.9) |
| GG2 | 94 (24.2) |
| ≥ GG3 | 31 (7.9) |
PSA: prostate-specific antigen; PSAD: prostate-specific antigen density; PI-RADS: Prostate Imaging Reporting and Data System; GG: Grade Group; CI: confindence interval.