| Literature DB >> 36033582 |
Huaqing Yan1, Yiming Wu1, Xiaobo Cui1, Sinian Zheng1, Peng Zhang1, Rubing Li1.
Abstract
Purpose: To access the incidence and predictors of Gleason grade group upgrading from cognitive MR-targeted fusion prostate biopsy to radical prostatectomy in a Chinese cohort. Materials andEntities:
Mesh:
Substances:
Year: 2022 PMID: 36033582 PMCID: PMC9402296 DOI: 10.1155/2022/7944342
Source DB: PubMed Journal: Biomed Res Int Impact factor: 3.246
Baseline characteristics of included patients.
| Age, mean ± SD (years) | 68.82 ± 6.45 |
| PSA, mean ± SD (ng/ml) | 19.00 ± 23.72 |
| Prostate volume, mean ± SD (ml) | 38.55 ± 22.06 |
| PSAD, mean ± SD | 0.67 ± 1.28 |
| Percent of positive cores, mean ± SD | 0.42 ± 0.29 |
| Patients with MRI-visible prostate lesions, | 182 (91.5) |
| Clinical stage, | |
| T1c | 12 (6.0) |
| T2a | 23 (11.6) |
| T2b | 64 (32.2) |
| T2c | 77 (38.6) |
| ≥T3 | 23 (11.6) |
Gleason grade groups on prostate biopsy and radical prostatectomy.
| Gleason grade group at biopsy | Gleason grade group at radical prostatectomy | Total | ||||
|---|---|---|---|---|---|---|
| 3+3 (GR1) | 3+4 (GR2) | 4+3 (GR3) | 8 (GR4) | 9-10 (GR5) | ||
| 3+3 (GR1) | 17 | 25 | 8 | 4 | 0 | 54 |
| 3+4 (GR2) | 2 | 24 | 11 | 6 | 2 | 45 |
| 4+3 (GR3) | 0 | 5 | 22 | 6 | 4 | 37 |
| 8 (GR4) | 0 | 1 | 8 | 14 | 14 | 37 |
| 9-10 (GR5) | 0 | 0 | 2 | 1 | 23 | 26 |
| Total | 19 | 55 | 51 | 31 | 43 | 199 |
Multivariable logistic regression models to predict prostate Gleason grade group upgrading.
| Predictors | Upgrade | ||
|---|---|---|---|
| OR | 95% CI |
| |
| Biopsy GR | |||
| GR1 | 1.000 (reference) | ||
| GR2 | 0.288 | 0.122-0.681 | 0.005 |
| GR3 | 0.159 | 0.061-0.414 | <0.001 |
| GR4 | 0.223 | 0.088-0.564 | 0.002 |
| GR5 | 0 | 0 | 0.998 |
| PV | 0.985 | 0.970-1.000 | 0.043 |
| Patient year | 1.068 | 1.013-1.125 | 0.015 |
| AUC:0.775 (0.712-0.839) | |||
Figure 1The ROC curve of the logistic regression model. The AUC of the model was 0.775 (95% CI 0.712-0.839).
Figure 2Nomograms for predicting prostate Gleason grade group upgrading. Instructions: To reach the predicted probability, locate the patient data at each axis and draw a vertical line to the “Points” axis and read the values. Sum all the points. Locate the sum to the “Total Points” axis and to draw a vertical line to the “Risk” axis to obtain the probability of upgrading or downgrading.