Predicting Biochemical Recurrence After Robot-Assisted Prostatectomy with Interpretable Machine Learning Model
Abstract
1. Introduction
2. Materials and Methods
2.1. Study Design and Patients
2.2. Data Collection
2.3. Feature Selection
2.4. Model Building
2.5. Model Interpretation
2.6. Statistical Analysis
3. Results
3.1. Patient Characteristics
3.2. Feature Selection
3.3. Model Building and Performance Evaluation
3.4. Interpretation of the Optimal Model with SHAP
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
BCR | Biochemical recurrence |
RARP | Robot-assisted radical prostatectomy |
ML | Machine learning |
LR | Logistic regression |
KNN | K-nearest neighbor |
MLP | Multilayer perceptron |
SVM | Support vector machine |
LightGBM | Light gradient boosting machine |
LASSO | Least absolute shrinkage and selection operator |
ROC | receiver operating characteristic |
AUC | Area under the curve |
SHAP | Shapley additive explanations |
cT | Clinical T stage |
pT | Pathological T stage |
PSM | Positive surgical margin |
SVI | Seminal vesicle invasion |
PI | Perineural invasion |
pGG | Pathological International Society of Urological Pathology Grade Group |
PSA | Prostate-specific antigen |
iPSA | Initial prostate-specific antigen |
TST | Testosterone |
DCA | Decision curve analysis |
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Feature Name | No BCR | BCR | p Value |
---|---|---|---|
Age (years) | 66.97 ± 6.56 | 67.34 ± 7.00 | 0.496 |
BMI (kg/m2) | 24.24 ± 3.24 | 24.19 ± 2.57 | 0.839 |
Prostate volume (ml) | 32.07 (24.55, 35.0) | 29 (21.33, 33.3) | 0.005 |
iPSA (ng/mL) | 6.5 (5, 8.89) | 8.165 (6.11, 12.66) | <0.001 |
PSAD (ng/mL2) | 0.28 (0.18, 0.29) | 0.29 (0.23, 0.42) | <0.001 |
PSA nadir (ng/mL) | 0.004 (0.002, 0.007) | 0.005 (0.002, 0.013) | <0.001 |
TST (ng/mL) | 4.6 (3.46, 5.91) | 4.675 (3.68, 6.17) | 0.422 |
Systematic prostate biopsy positive rate | 0.21 (0.13, 0.33) | 0.31 (0.19, 0.5) | <0.001 |
cT, n (%) | <0.001 | ||
1 | 171 (18.1%) | 25 (14%) | |
2 | 748 (79%) | 131 (73.6%) | |
3 | 28 (3%) | 22 (12.4%) | |
pT, n (%) | <0.001 | ||
1 | 2 (0.2%) | 0 (0%) | |
2 | 755 (79.7%) | 55 (30.9%) | |
3 | 190 (20.1%) | 123 (69.1%) | |
pGG, n (%) | 0.046 | ||
1 (3 + 3 = 6) | 149 (15.7%) | 21 (11.8%) | |
2 (3 + 4 = 7) | 406 (42.9%) | 66 (37.1%) | |
3 (4 + 3 = 7) | 231 (24.4%) | 59 (33.1%) | |
4 (5 + 3, 4 + 4, 3 + 5 = 8) | 109 (11.5%) | 26 (14.6%) | |
5 (5 + 4, 4 + 5, 5 + 5 = 10) | 52 (5.5%) | 6 (3.4%) | |
PSM, n (%) | <0.001 | ||
No | 722 (76.2%) | 85 (47.8%) | |
Yes | 225 (23.8%) | 93 (52.2%) | |
PI, n (%) | <0.001 | ||
No | 551 (58.2%) | 60 (33.7%) | |
Yes | 396 (41.8%) | 118 (66.3%) | |
SVI, n (%) | <0.001 | ||
No | 920 (97.1%) | 128 (71.9%) | |
Yes | 27 (2.9%) | 50 (28.1%) | |
D’Amico risk classification, n (%) | <0.001 | ||
Low | 173 (18.3%) | 21 (11.8%) | |
Middle | 604 (63.8%) | 79 (44.4%) | |
High | 170 (18%) | 78 (43.8%) |
Feature Name | Training Set | Testing Set | p Value |
---|---|---|---|
Age (years) | 68 (63, 72) | 68 (63, 72) | 0.932 |
BMI (kg/m2) | 24.14 (22.17, 26.08) | 23.72 (22.18, 25.53) | 0.174 |
Prostate volume (ml) | 32.07 (24, 34.55) | 30.9 (23.4, 35) | 0.191 |
iPSA (ng/mL) | 6.73 (5.15, 9.41) | 6.825 (5.12, 9.21) | 0.893 |
PSAD (ng/mL2) | 0.29 (0.18, 0.3) | 0.285 (0.19, 0.33) | 0.574 |
PSA nadir (ng/mL) | 0.004 (0.002, 0.008) | 0.004 (0.002, 0.007) | 0.911 |
TST (ng/mL) | 4.6 (3.49, 5.89) | 4.68 (3.51, 6.15) | 0.648 |
Systematic prostate biopsy positive rate | 0.25 (0.13, 0.38) | 0.25 (0.14, 0.4) | 0.195 |
cT, n (%) | 0.812 | ||
1 | 134 (17%) | 62 (18.3%) | |
2 | 619 (78.7%) | 260 (76.9%) | |
3 | 34 (4.3%) | 16 (4.7%) | |
pT, n (%) | 0.584 | ||
1 | 2 (0.3%) | 0 (0%) | |
2 | 563 (71.5%) | 247 (73.1%) | |
3 | 222 (28.2%) | 91 (26.9%) | |
pGG, n (%) | 0.406 | ||
1 (3 + 3 = 6) | 119 (15.1%) | 51 (15.1%) | |
2 (3 + 4 = 7) | 326 (41.4%) | 146 (43.2%) | |
3 (4 + 3 = 7) | 204 (25.9%) | 86 (25.4%) | |
4 (5 + 3, 4 + 4, 3 + 5 = 8) | 91 (11.6%) | 44 (13%) | |
5 (5 + 4, 4 + 5, 5 + 5 = 10) | 47 (6%) | 11 (3.3%) | |
PSM, n (%) | 0.617 | ||
No | 568 (72.2%) | 239 (70.7%) | |
Yes | 219 (27.8%) | 99 (29.3%) | |
PI, n (%) | 0.479 | ||
No | 422 (53.6%) | 189 (55.9%) | |
Yes | 365 (46.4%) | 149 (44.1%) | |
SVI, n (%) | 0.210 | ||
No | 738 (93.8%) | 310 (91.7%) | |
Yes | 49 (6.2%) | 28 (8.3%) | |
D’Amico risk classification, n (%) | 0.082 | ||
Low | 143 (18.2%) | 51 (15.1%) | |
Middle | 461 (58.6%) | 222 (65.7%) | |
High | 183 (23.3%) | 65 (19.2%) | |
BCR, n (%) | 0.792 | ||
No | 661 (84%) | 286 (84.6%) | |
Yes | 126 (16%) | 52 (15.4%) |
Model | Accuracy | AUC | 95% CI | Sensitivity | Specificity | F1 |
---|---|---|---|---|---|---|
LR | 0.808 | 0.802 | 0.729–0.874 | 0.692 | 0.829 | 0.526 |
KNN | 0.666 | 0.806 | 0.740–0.872 | 0.846 | 0.633 | 0.438 |
MLP | 0.814 | 0.813 | 0.748–0.877 | 0.615 | 0.85 | 0.504 |
SVM | 0.763 | 0.849 | 0.787–0.911 | 0.788 | 0.759 | 0.515 |
LightGBM | 0.731 | 0.881 | 0.840–0.922 | 0.942 | 0.692 | 0.524 |
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Zhang, T.; Ide, H.; Lu, J.; Lu, Y.; China, T.; Nagata, M.; Hachiya, T.; Horie, S. Predicting Biochemical Recurrence After Robot-Assisted Prostatectomy with Interpretable Machine Learning Model. J. Clin. Med. 2025, 14, 7079. https://doi.org/10.3390/jcm14197079
Zhang T, Ide H, Lu J, Lu Y, China T, Nagata M, Hachiya T, Horie S. Predicting Biochemical Recurrence After Robot-Assisted Prostatectomy with Interpretable Machine Learning Model. Journal of Clinical Medicine. 2025; 14(19):7079. https://doi.org/10.3390/jcm14197079
Chicago/Turabian StyleZhang, Tianwei, Hisamitsu Ide, Jun Lu, Yan Lu, Toshiyuki China, Masayoshi Nagata, Tsuyoshi Hachiya, and Shigeo Horie. 2025. "Predicting Biochemical Recurrence After Robot-Assisted Prostatectomy with Interpretable Machine Learning Model" Journal of Clinical Medicine 14, no. 19: 7079. https://doi.org/10.3390/jcm14197079
APA StyleZhang, T., Ide, H., Lu, J., Lu, Y., China, T., Nagata, M., Hachiya, T., & Horie, S. (2025). Predicting Biochemical Recurrence After Robot-Assisted Prostatectomy with Interpretable Machine Learning Model. Journal of Clinical Medicine, 14(19), 7079. https://doi.org/10.3390/jcm14197079