Developing a Predictive Model for Significant Prostate Cancer Detection in Prostatic Biopsies from Seven Clinical Variables: Is Machine Learning Superior to Logistic Regression?
Simple Summary
Abstract
1. Introduction
2. Materials and Methods
2.1. Study Design and Participants
2.2. Diagnostic Approach for Significant Prostate Cancer
2.3. Predictive Variables Included in the Models and Outcome Variable
2.4. Algorithms Used for Model Development
2.5. Statistical Analyses, Algorithm Performance, and Interpretation
3. Results
3.1. Participant Characteristics
3.2. Model Performance, Calibration, and Validation of the GMV and BCN Predictive Models
3.3. Variable Importance Interpretation with SHapley Additive exPlanations (SHAP)
3.4. Clinical Comparison of GMV and BCN Predictive Models for sPCa Detection
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Characteristic | Development Cohort | Validation Cohort | Test Cohort | p-Value |
---|---|---|---|---|
Number of men | 4254 | 639 | 751 | |
Mean age at biopsy, years (SD) | 68 (8.3) | 68 (8.3) | 68 (8.2) | 1 |
Mean serum PSA, ng/mL (SD) | 13.4 (75.0) | 12.7 (35.5) | 12.8 (47.6) | 0.36 |
Suspicious DRE, n (%) | 1210 (28.5) | 192 (30.1) | 217 (28.9) | 0.11 |
PCa family history, n (%) | 304 (7.2) | 51 (8.0) | 48 (6.4) | 0.2 |
Mean prostate volume, mL (SD) | 61.5 (32.3) | 62.0 (33.1) | 64.6 (35.8) | 0.65 |
Previous negative prostate biopsy, n (%) | 1281 (30.2) | 216 (33.9) | 224 (29.9) | 0.41 |
PI-RADS version used | 2 | 2.1 | 2 | |
Mean number of suspicious lesions | 2 | 2 | 2 | |
PI-RADS score of index lesion, n (%) | ||||
1 | 470 (11.1) | 71 (11.2) | 104 (13.9) | 0.33 |
2 | 148 (3.5) | 15 (2.4) | 27 (3.6) | 0.09 |
3 | 1053 (24.8) | 161 (25.2) | 197 (26.3) | 0.27 |
4 | 1743 (41.0) | 261 (40.9) | 272 (36.3) | 0.07 |
5 | 840 (19.8) | 131 (20.6) | 151 (20.2) | 0.71 |
sPCa detection, n (%) | 1782 (41.9) | 268 (42.0) | 315 (42.0) | 1 |
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Characteristic | sPCa | nsPCa | Odds Ratio (95% CI) | p-Value |
---|---|---|---|---|
Number of men (%) | 2097 (41.9) | 2908 (58.1) | - | - |
Mean age, years (SD) | 70 (8.2) | 66 (7.6) | 1.07 (1.06–1.08) | <0.001 |
Mean serum PSA, ng/mL (SD) | 20 (109) | 8.4 (9.6) | 1.04 (1.03–1.05) | <0.001 |
PCa family history, n (%) | ||||
No | 1930 (92%) | 2723 (93.6%) | - | Ref. |
Yes | 167 (8%) | 185 (6.4%) | 1.27 (1.02–1.58) | 0.033 |
Type of prostate biopsy, n (%) | ||||
Initial | 1594 (76%) | 1906 (65.5%) | - | Ref. |
Repeated | 503 (24%) | 1002 (34.5%) | 0.6 (0.53–0.68) | <0.001 |
DRE, n (%) | ||||
Normal | 1161 (55.4%) | 2417 (83.1%) | - | Ref. |
Suspicious | 936 (44.6%) | 491 (16.9%) | 3.97 (3.49–4.52) | <0.001 |
Prostate volume (mL) | 51.9 (27.6) | 69.1 (34.4) | 0.98 (0.98–0.98) | <0.001 |
PI-RADS score, n (%) | ||||
1 | 60 (2.9%) | 514 (17.7%) | - | Ref. |
2 | 23 (1.1%) | 152 (5.2%) | 1.3 (0.76–2.14) | 0.322 |
3 | 206 (9.8%) | 1044 (35.9%) | 1.69 (1.25–2.31) | 0.001 |
4 | 991 (47.3%) | 1024 (35.2%) | 8.29 (6.31–11.08) | <0.001 |
5 | 817 (39%) | 174 (6%) | 40.22 (29.61–55.48) | <0.001 |
Metric | Training Set (n = 4–254) | Validation Set (n = 631) | Test Set (n = 751) | |||
---|---|---|---|---|---|---|
GMV Model | BCN Model | GMV Model | BCN Model | GMV Model | BCN Model | |
AUC (95% CI) | 0.88 (0.87−0.90) | 0.85 (0.84−0.86) | 0.88 (0.86−0.91) | 0.86 (0.85−0.87) | 0.85 (0.83−0.88) | 0.84 (0.82−0.86) |
Precision (95% CI) | 0.7171 (0.6973−0.7362) | 0.7435 (0.7228−0.7633) | 0.7184 (0.6659−0.7657) | 0.7426 (0.6876−0.7910) | 0.7126 (0.6618−0.7585) | 0.7607 (0.7074−0.8069) |
Recall (95% CI) | 0.8266 (0.8083−0.8435) | 0.7435 (0.7228−0.7633) | 0.8284 (0.7787−0.8688) | 0.7537 (0.6988−0.8015) | 0.7556 (0.7052−0.7997) | 0.6762 (0.6227−0.7255) |
Specificity (95% CI) | 0.765 (0.7478−0.7813) | 0.8151 (0.7993−0.8299) | 0.7655 (0.7198−0.8058) | 0.8113 (0.7684−0.8479) | 0.7798 (0.7386−0.8162) | 0.8463 (0.8095−0.8771) |
Accuracy (95% CI) | 0.7908 (0.7783−0.8027) | 0.7851 (0.7725−0.7972) | 0.7919 (0.7587−0.8215) | 0.7872 (0.7538−0.8171) | 0.7696 (0.7382−0.7983) | 0.7750 (0.7437−0.8034) |
F1 score (95% CI) | 0.768 (0.7537−0.7832) | 0.7435 (0.7275−0.7592) | 0.7695 (0.7298−0.8047) | 0.7481 (0.7025−0.7842) | 0.7334 (0.6952−0.7695) | 0.7160 (0.6736−0.7537) |
Kappa score (95% CI) | 0.5792 (0.5548−0.6037) | 0.5587 (0.5325−0.5847) | 0.5815 (0.5163−0.6389) | 0.5639 (0.4903−0.6276) | 0.5309 (0.4671−0.5851) | 0.5307 (0.4671−0.5911) |
MCC (95% CI) | 0.5841 (0.5603−0.6085) | 0.5587 (0.5325−0.5850) | 0.5864 (0.5243−0.6441) | 0.5639 (0.4910−0.6287) | 0.5316 (0.4682−0.5857) | 0.5332 (0.4685−0.5948) |
Threshold (%) | GMV Model | BCN Model | ||
---|---|---|---|---|
Saved Biopsies (%) | Undetected sPCa (%) | Saved Biopsies (%) | Undetected sPCa (%) | |
5 | 3.5 | 0.5 | 18 | 2 |
6 | 5 | 0.75 | 20 | 2.5 |
7 | 9.5 | 1 | 23 | 3.5 |
8 | 10 | 2 | 26 | 4.75 |
9 | 14 | 2 | 27 | 5 |
10 | 17 | 2 | 27.5 | 5 |
11 | 19 | 2.5 | 30 | 6 |
12 | 20 | 2.5 | 32.5 | 6.5 |
13 | 22 | 2.6 | 33.5 | 6.5 |
14 | 23 | 2.6 | 34 | 8 |
15 | 26 | 4.5 | 35 | 8.5 |
16 | 27.5 | 5 | 36 | 10.5 |
17 | 29 | 5 | 37.5 | 10.5 |
18 | 30 | 5.1 | 39.5 | 13 |
19 | 30 | 5.1 | 40 | 13 |
20 | 32.5 | 6.5 | 41 | 13 |
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Morote, J.; Miró, B.; Hernando, P.; Paesano, N.; Picola, N.; Muñoz-Rodriguez, J.; Ruiz-Plazas, X.; Muñoz-Rivero, M.V.; Celma, A.; García-de Manuel, G.; et al. Developing a Predictive Model for Significant Prostate Cancer Detection in Prostatic Biopsies from Seven Clinical Variables: Is Machine Learning Superior to Logistic Regression? Cancers 2025, 17, 1101. https://doi.org/10.3390/cancers17071101
Morote J, Miró B, Hernando P, Paesano N, Picola N, Muñoz-Rodriguez J, Ruiz-Plazas X, Muñoz-Rivero MV, Celma A, García-de Manuel G, et al. Developing a Predictive Model for Significant Prostate Cancer Detection in Prostatic Biopsies from Seven Clinical Variables: Is Machine Learning Superior to Logistic Regression? Cancers. 2025; 17(7):1101. https://doi.org/10.3390/cancers17071101
Chicago/Turabian StyleMorote, Juan, Berta Miró, Patricia Hernando, Nahuel Paesano, Natàlia Picola, Jesús Muñoz-Rodriguez, Xavier Ruiz-Plazas, Marta V. Muñoz-Rivero, Ana Celma, Gemma García-de Manuel, and et al. 2025. "Developing a Predictive Model for Significant Prostate Cancer Detection in Prostatic Biopsies from Seven Clinical Variables: Is Machine Learning Superior to Logistic Regression?" Cancers 17, no. 7: 1101. https://doi.org/10.3390/cancers17071101
APA StyleMorote, J., Miró, B., Hernando, P., Paesano, N., Picola, N., Muñoz-Rodriguez, J., Ruiz-Plazas, X., Muñoz-Rivero, M. V., Celma, A., García-de Manuel, G., Servian, P., Abascal, J. M., Trilla, E., & Méndez, O. (2025). Developing a Predictive Model for Significant Prostate Cancer Detection in Prostatic Biopsies from Seven Clinical Variables: Is Machine Learning Superior to Logistic Regression? Cancers, 17(7), 1101. https://doi.org/10.3390/cancers17071101