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Journal of Peking University (Health Sciences) ›› 2023, Vol. 55 ›› Issue (5): 818-824. doi: 10.19723/j.issn.1671-167X.2023.05.007

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Predictive model of early urinary continence recovery based on prostate gland MRI parameters after laparoscopic radical prostatectomy

Hai MAO1,2,Fan ZHANG1,Zhan-yi ZHANG1,Ye YAN1,Yi-chang HAO1,Yi HUANG1,*(),Lu-lin MA1,Hong-ling CHU3,*(),Shu-dong ZHANG1   

  1. 1. Department of Urology, Peking University Third Hospital, Beijing 100191, China
    2. Department of Urology, Traditional Chinese Medicine Hospital of Fengjie, Fengjie 404600, Chongqing, China
    3. Research Center of Clinical Epidemiology, Peking University Third Hospital, Beijing 100191, China
  • Received:2023-04-04 Online:2023-10-18 Published:2023-10-09
  • Contact: Yi HUANG,Hong-ling CHU E-mail:pku_huang@163.com;18810530974@163.com

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Abstract:

Objective: Constructing a predictive model for urinary incontinence after laparoscopic radical prostatectomy (LRP) based on prostatic gland related MRI parameters. Methods: In this study, 202 cases were included. All the patients were diagnosed with prostate cancer by prostate biopsy and underwent LRP surgery in Peking University Third Hospital. The preoperative MRI examination of all the patients was completed within 1 week before the prostate biopsy. Prostatic gland related parameters included prostate length, width, height, prostatic volume, intravesical prostatic protrusion length (IPPL), prostate apex shape, etc. From the first month after the operation, the recovery of urinary continence was followed up every month, and the recovery of urinary continence was based on the need not to use the urine pad all day long. Logistic multivariate regression analysis was used to analyze the influence of early postoperative recovery of urinary continence. Risk factors were used to draw the receiver operator characteristic (ROC) curves of each model to predict the recovery of postoperative urinary continence, and the difference of the area under the curve (AUC) was compared by DeLong test, and the clinical net benefit of the model was evaluated by decision curve analysis (DCA). Results: The average age of 202 patients was 69.0 (64.0, 75.5) years, the average prostate specific antigen (PSA) before puncture was 12.12 (7.36, 20.06) μg/L, and the Gleason score < 7 points and ≥ 7 points were 73 cases (36.2%) and 129 cases (63.9%) respectively, with 100 cases (49.5%) at T1/T2 clinical stage, and 102 cases (50.5%) at T3 stage. The prostatic volume measured by preoperative MRI was 35.4 (26.2, 51.1) mL, the ratio of the height to the width was 0.91 (0.77, 1.07), the membranous urethral length (MUL) was 15 (11, 16) mm, and the IPPL was 2 (0, 6) mm. The prostatic apex A-D subtypes were 67 cases (33.2%), 80 cases (39.6%), 24 cases (11.9%) and 31 cases (15.3%), respectively. The training set and validation set were 141 cases and 61 cases, respectively. The operations of all the patients were successfully completed, and the urinary continence rate was 59.4% (120/202) in the 3 months follow-up. The results of multivariate analysis of the training set showed that the MUL (P < 0.001), IPPL (P=0.017) and clinical stage (P=0.022) were independent risk factors for urinary incontinence in the early postoperative period (3 months). The nomogram and clinical decision curve were made according to the results of multivariate analysis. The AUC value of the training set was 0.885 (0.826, 0.944), and the AUC value of the validation set was 0.854 (0.757, 0.950). In the verification set, the Hosmer-Lemeshow goodness-of-fit test was performed on the model, and the Chi-square value was 5.426 (P=0.711). Conclusion: Preoperative MUL, IPPL, and clinical stage are indepen-dent risk factors for incontinence after LRP. The nomogram developed based on the relevant parameters of MRI glands can effectively predict the recovery of early urinary continence after LRP. The results of this study require further large-scale clinical research to confirm.

Key words: Prostate neoplasm, Urinary continence, Laparoscopic surgery, Prostatectomy, Nomogram

CLC Number: 

  • R737.25

Table 1

Comparison of patients characteristics between training dataset and validation dataset"

Items All patients
(n=202)		 Training dataset
(n=141)		 Validation dataset
(n=61)
Age/years, n (%)
  ≥70 109 (54.0) 77 (54.6) 32 (52.5)
   < 70 93 (46.0) 64 (45.4) 29 (47.5)
Pre-biopsy PSA/(μg/L), M (P25, P75) 12.12 (7.36, 20.06) 10.98 (7.43, 19.31) 12.90 (7.10, 21.80)
Biopsy Gleason score, n (%)
  ＜7 73 (36.1) 56 (39.7) 17 (27.9)
  ≥7 129 (63.9) 85 (60.3) 44 (72.1)
cT, n (%)
  T1/T2 100 (49.5) 66 (46.8) 34 (55.7)
  T3 102 (50.5) 75 (53.2) 27 (44.3)
PV/mL, M (P25, P75) 35.4 (26.2, 51.1) 35.8 (27.1, 50.5) 33.9 (23.4, 53.7)
MUL/mm, M (P25, P75) 15 (11, 16) 15 (11, 16) 15 (11, 17)
IPPL/mm, M (P25, P75) 2 (0, 6) 2 (0, 6) 2 (0, 8)
Length/width, M (P25, P75) 0.91 (0.77, 1.07) 0.91 (0.82, 1.07) 0.89 (0.74, 1.06)
Prostate apex shape, n (%)
  A-C 171 (84.7) 122 (86.5) 49 (80.3)
  D 31 (15.3) 19 (13.5) 12 (19.7)

Table 2

Univariate analysis of continence recovery 3 months of training dataset after surgery"

Items Continence
(n=58)		 Incontinence
(n=83)		 t/χ2/z value P value
Age/years, n (%) 1.595 0.207
  ≥70 28 (48.3) 49 (59.0)
   < 70 30 (51.7) 34 (41.0)
Pre-biopsy PSA/(μg/L), M (P25, P75) 13.59 (7.62, 22.31) 10.27 (7.16, 17.15) 0.953 0.340
Biopsy Gleason score, n (%) 0.114 0.736
  ＜7 24 (41.4) 32 (38.6)
  ≥7 34 (58.6) 51 (61.4)
cT, n (%) 6.012 0.014
  T1/T2 20 (34.5) 46 (55.4)
  T3 38 (65.5) 37 (44.6)
PV/mL, M (P25, P75) 41.51 (31.49, 56.42) 34.53 (23.7, 44.33) 2.786 0.005
MUL/mm, M (P25, P75) 11 (9, 14) 15 (14, 17) -6.059 < 0.001
IPPL/mm, M (P25, P75) 6 (0, 10) 0 (0, 3) 4.788 < 0.001
Prostate apex shape, n (%) 3.657 0.056
  A-C 54 (93.1) 68 (81.9)
  D 4 (6.9) 15 (18.1)
Length/width, M (P25, P75) 0.99 (0.85, 1.13) 0.88 (0.77, 1.01) 2.506 0.012

Table 3

Multivariate analysis of continence recovery 3 months"

Items B S.E. z P OR (95%CI)
IPPL -0.140 0.059 -2.384 0.017 0.87 (0.78, 0.98)
MUL 0.367 0.073 5.021 < 0.001 1.44 (1.25, 1.67)
cT -1.086 0.475 -2.286 0.022 0.34 (0.13, 0.86)
Length/width -2.476 1.269 -1.950 0.051 0.08 (0.01, 1.01)
Constant -0.143 1.597 -0.089 0.929

Figure 1

ROC curves of continence recovery after LRP of training dataset (A) and validation dataset (B) LRP, laparoscopic radical prostatectomy; ROC, receiver operator characteristic."

Figure 2

Calibration plot of continence recovery after LRP of training dataset (A) and validation dataset (B) LRP, laparoscopic radical prostatectomy."

Figure 3

The nomogram (A) and DCA (B) of continence recovery after LRP of training dataset and validation dataset MUL, membranous urethral length; IPPL, intravesical prostatic protrusion length; cT, clinical stage; LRP, laparoscopic radical prostatectomy; DCA, decision curve analysis."

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