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Journal of Peking University (Health Sciences) ›› 2025, Vol. 57 ›› Issue (4): 684-691. doi: 10.19723/j.issn.1671-167X.2025.04.009

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A preoperative prediction model for pelvic lymph node metastasis in prostate cancer: Integrating clinical characteristics and multiparametric MRI

Zeyuan WANG, Shuanbao YU, Haoke ZHENG, Jin TAO, Yafeng FAN, Xuepei ZHANG*()   

  1. Department of Urology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450000, China
  • Received:2025-02-27 Online:2025-08-18 Published:2025-08-02
  • Contact: Xuepei ZHANG
  • Supported by:
    the Youth Science Fund of Henan Province(232300420254); the Key Scientific and Technological Projects of Henan Province(242102311074)

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Abstract:

Objective: To analyze the clinical features associated with pelvic lymph node metastasis (PLNM) in prostate cancer and to construct a preoperative prediction model for PLNM, thereby reducing unnecessary extended pelvic lymph node dissection (ePLND). Methods: Based on predefined inclusion and exclusion criteria, 344 patients who underwent radical prostatectomy and ePLND at the First Affiliated Hospital of Zhengzhou University between 2014 and 2024 were retrospectively enrolled, among whom, 77 patients (22.4%) were pathologically confirmed to have lymph node-positive disease. The clinical characteristics, MRI reports, and pathological results were collected. The data were then randomly divi-ded into a training cohort (241 cases, 70%) and a validation cohort (103 cases, 30%). Univariate and multivariate Logistic regression analysis were employed to construct a preoperative prediction model for PLNM. Results: Univariate Logistic regression analysis revealed that total prostate specific antigen (tPSA) (P=0.021), free prostate specific antigen (fPSA) (P=0.002), fPSA to tPSA ratio (fPSA/tPSA) (P=0.011), percentage of positive biopsy cores (P < 0.001), prostate imaging reporting and data system (PI-RADS) score (P=0.004), biopsy Gleason score ≥8 (P=0.005), clinical T stage (P < 0.001), and MRI-indicated lymph node involvement (MRI-LNI) (P < 0.001) were significant predictors of PLNM. Multivariate Logistic regression analysis demonstrated that the percentage of positive biopsy cores (OR=91.24, 95%CI: 13.34-968.68), PI-RADS score (OR=7.64, 95%CI: 1.78-138.06), and MRI-LNI (OR=4.67, 95%CI: 1.74-13.24) were independent risk factors for PLNM. And a novel nomogram for predicting PLNM was developed by integrating all these three variables. Compared with the individual predictors: percentage of positive biopsy cores [area under curve (AUC)=0.806], PI-RADS score (AUC=0.679), and MRI-LNI (AUC=0.768), the multivariate model incorporating all three variables demonstrated significantly superior predictive performance (AUC=0.883). Consistently, calibration curves and decision curve analyses confirmed that the multivariable model had high predictive accuracy and provided significant net clinical benefit relative to single-variable models. And using a cutoff of 6%, the multiparameter model missed only approximately 5.2% of PLNM cases (4/77), while reducing approximately 53% of ePLND procedures (139/267), demonstrating favorable predictive efficacy. Conclusion: Percentage of positive biopsy cores, PI-RADS score and MRI-LNI are independent risk factors for PLNM. The constructed multivariate model significantly improves predictive efficacy, offering a valuable tool to guide clinical decisions on ePLND.

Key words: Prostatic neoplasms, Lymphatic metastasis, Multiparametric magnetic resonance imaging, Biopsy

CLC Number: 

  • R737.25

Table 1

Comparison of clinical characteristics between pelvic lymph node metastasis and non-metastasis groups"

Parameter Total (n=344) Pelvic lymph node metastasis Statistic P
Positive (n=77) Negative (n=267)
Age/years, M (P25, P75) 68 (64, 73) 68 (64, 73) 68 (64, 75) Z=-0.39 0.699
BMI/(kg/m2), M (P25, P75) 24.9 (23.0, 26.7) 25.4 (23.7, 26.6) 24.8 (22.9, 26.8) Z=-0.71 0.475
tPSA/(ng/mL), M (P25, P75) 19.5 (11.6, 50.4) 48.5 (24.2, 100.0) 16.8 (10.1, 34.3) Z=-6.44 < 0.001
fPSA/(ng/mL), M (P25, P75) 2.28 (1.13, 5.57) 5.28 (2.71, 11.90) 1.68 (0.99, 4.02) Z=-6.03 < 0.001
fPSA/tPSA, M (P25, P75) 0.103 (0.074, 0.150) 0.121 (0.082, 0.175) 0.098 (0.071, 0.148) Z=-2.59 0.01
PSAD/(ng/mL2), M (P25, P75) 0.62 (0.33, 1.24) 1.08 (0.61, 2.18) 0.54 (0.28, 0.95) Z=-4.90 < 0.001
Volume/mL, M (P25, P75) 40 (29, 59) 46 (34, 71) 38 (28, 55) Z=-2.30 0.021
Positive biopsy cores/%, M (P25, P75) 0.63 (0.38, 1.00) 1.00 (0.92, 1.00) 0.53 (0.29, 0.85) Z=-7.84 < 0.001
PI-RADS score, n (%) χ2=31.77 < 0.001
  ≤2 25 (7.27) 0 (0) 25 (9.36)
  3 65 (18.90) 1 (1.30) 64 (23.97)
  ≥4 254 (73.84) 76 (98.70) 178 (66.67)
Biopsy Gleason score, n (%) χ2=42.01 < 0.001
  3+3 48 (13.95) 3 (3.90) 45 (16.85)
  3+4 65 (18.90) 7 (9.09) 58 (21.72)
  4+3 74 (21.51) 7 (9.09) 67 (25.09)
  ≥8 157 (45.64) 60 (77.92) 97 (36.33)
Clinical T stage, n (%) χ2=47.16 < 0.001
  ≤T2 249 (72.38) 32 (41.56) 217 (81.27)
  >T2 95 (27.62) 45 (58.44) 50 (18.73)
MRI-LNI, n (%) χ2=75.97 < 0.001
  N0 284 (82.56) 38 (49.35) 246 (92.13)
  N1 60 (17.44) 39 (50.65) 21 (7.87)

Table 2

Comparison of baseline characteristics between training and validation cohort"

Characteristic Training cohort (n=241) Validation cohort (n=103) Statistic P
Age/years, M (P25, P75) 69 (64, 73) 68 (64, 74) Z=-0.05 0.959
BMI/(kg/m2), M (P25, P75) 25.0 (22.8, 26.8) 4.5 (23.5, 25.9) Z=-0.35 0.726
tPSA/(ng/mL), M (P25, P75) 19.1 (11.1, 47.7) 20.2 (13.1, 51.7) Z=-0.88 0.376
fPSA/(ng/mL), M (P25, P75) 2.41 (1.07, 5.39) 2.20 (1.28, 6.12) Z=-0.52 0.606
Positive biopsy cores/%, M (P25, P75) 0.62 (0.39, 1.00) 0.63 (0.38, 0.92) Z=-0.78 0.433
Volume/mL, M (P25, P75) 39.0 (29.0, 55.0) 43.0 (27.5, 61.5) Z=-0.66 0.511
PSAD/(ng/mL2), M (P25, P75) 0.63 (0.32, 1.23) 0.62 (0.33, 1.25) Z=-0.09 0.929
fPSA/tPSA, M (P25, P75) 0.10 (0.08, 0.14) 0.11 (0.07, 0.16) Z=-0.001 0.999
PI-RADS score, n (%) χ2=0.09 0.955
  ≤2 17 (7.05) 8 (7.77)
  3 45 (18.67) 20 (19.42)
  ≥4 179 (74.27) 75 (72.82)
Biopsy Gleason score, n (%) χ2=3.17 0.365
  3+3 29 (12.03) 19 (18.45)
  3+4 49 (20.33) 16 (15.53)
  4+3 51 (21.16) 23 (22.33)
  ≥8 112 (46.47) 45 (43.69)
Clinical T stage, n (%) χ2=2.06 0.152
  ≤T2 169 (70.12) 80 (77.67)
  >T2 72 (29.88) 23 (22.33)
MRI-LNI, n (%) χ2=0.30 0.583
  N0 189 (78.42) 78 (75.73)
  N1 52 (21.58) 25 (24.27)

Table 3

Univariate and multivariate Logistic regression analyses of clinical parameters for predicting PLNM in the training cohort"

Characteristic Univariate analysis Multivariable analysis
OR (95%CI) Z P Coefficient Z OR (95%CI) P
Age 1.006 (0.957, 1.057) 0.231 0.817
BMI 1.093 (0.924, 1.294) 1.039 0.299
tPSA 1.007 (1.001, 1.014) 2.315 0.021
fPSA 1.099 (1.037, 1.166) 3.171 0.002
fPSA/tPSA 591.8 (4.3, 81 857.0) 2.538 0.011
PSAD 1.111 (0.926, 1.333) 1.136 0.256
PV 1.008 (0.998, 1.019) 1.556 0.120
Positive biopsy cores 232.4 (30.3, 1 780.0) 5.245 < 0.001 4.513 4.188 91.24 (13.34, 968.68) < 0.001
PI-RADS score 18.30 (2.56, 130.60) 2.898 0.004 2.034 2.078 7.64 (1.78, 138.06) 0.038
Biopsy Gleason score
  3+3 1.000 (Reference)
  3+4 0.880 (0.138, 5.606) -0.135 0.893
  4+3 1.149 (0.197, 6.692) 0.154 0.877
≥8 8.413 (1.904, 37.180) 2.809 0.005
Clinical T stage 0.150 (0.077, 0.291) -5.594 < 0.001
MRI-LNI 9.268 (4.386, 19.580) 5.834 < 0.001 1.540 2.995 4.67 (1.74, 13.24) 0.003

Figure 1

Nomogram for predicting PLNM in prostate cancer PLNM, pelvic lymph node metastasis; PI-RADS, prostate imaging reporting and data system; MRI-LNI, MRI-indicated lymph node involvement."

Figure 2

ROC curve, calibration plots and DCA of MRI-LNI, PI-RADS, percentage of positive biopsy cores, and the multivariable model for suspected PLNM A, ROC curve in the training cohort; B, calibration plot in the training cohort; C, DCA in the training cohort; D, ROC curve in the validation cohort; E, calibration plot in the validation cohort; F, DCA in the validation cohort. ROC, receiver operating characteristic; DCA, decision curve analysis; PLNM, pelvic lymph node metastasis; PI-RADS, prostate imaging reporting and data system; MRI-LNI, MRI-indicated lymph node involvement."

Table 4

Diagnostic performance of the multivariable model at different cutoff values for PLNM"

Cutoff value Below the cutoff (PLND not recommended), n (%) Above the cutoff (PLND recommended), n (%)
Total Without PLNM With PLNM Total Without PLNM With PLNM
2% 92 (26.7) 90 (97.8) 2 (2.2) 252 (73.3) 177 (70.2) 75 (29.8)
3% 111 (32.3) 108 (97.3) 3 (2.7) 233 (67.7) 159 (68.2) 74 (31.8)
4% 120 (34.9) 116 (96.7) 4 (3.3) 224 (65.1) 151 (67.4) 73 (32.6)
5% 129 (37.5) 125 (96.9) 4 (3.1) 215 (62.5) 142 (66.0) 73 (34.0)
6% 143 (41.6) 139 (97.2) 4 (2.8) 201 (58.4) 128 (63.7) 73 (36.3)
7% 146 (42.4) 141 (96.6) 5 (3.4) 198 (57.6) 126 (63.6) 72 (36.4)
8% 159 (46.2) 152 (95.6) 7 (4.4) 185 (53.7) 115 (62.2) 70 (37.8)
9% 167 (48.5) 159 (95.2) 8 (4.8) 177 (51.5) 108 (61.0) 69 (39.0)
10% 182 (52.9) 174 (95.6) 8 (4.4) 162 (47.1) 93 (57.4) 69 (42.6)
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