Prediction of Glomerular Filtration Rate Following Partial Nephrectomy for Localized Renal Cell Carcinoma with Different Machine Learning Techniques
Simple Summary
Abstract
1. Introduction
2. Materials and Methods
2.1. Data Collection
2.2. Ethics Statement
2.3. Statistical Analysis
3. Results
3.1. Baseline Perioperative Characteristics
3.2. Renal Function
3.3. Pathological Outcomes
3.4. Univariable Linear Regression Analyses
3.5. Multivariable Linear Regression with Preoperative Variables
3.6. Various Machine Learning Techniques
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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General Characteristics | No. of Pts./Median | %/IQR | |
---|---|---|---|
Gender | Female | 141 | 33.98 |
Male | 274 | 60.02 | |
Age | 63 | 54–69 | |
Body mass index | kg/m2 | 25.2 | 23.0–28.7 |
Preoperative characteristics | |||
Clinical T stage | cT1a | 348 | 83.86 |
cT1b | 62 | 14.94 | |
cT2a | 3 | 0.72 | |
cT2b | 2 | 0.48 | |
cT3 | 0 | 0 | |
Tumor diameter on CT/MRI | cm | 3.07 | 2.0–3.8 |
Charlson Comorbidity Index | median | 4.0 | 3.0–5.0 |
Diabetes | no | 345 | 83.13 |
yes | 54 | 13.01 | |
unknown | 16 | 3.86 | |
Hypertension | no | 183 | 44.10 |
yes | 231 | 55.66 | |
unknown | 1 | 0.24 | |
Coronary Artery Disease | no | 344 | 82.89 |
yes | 55 | 13.25 | |
unknown | 16 | 3.86 | |
Myocardial infarction | no | 377 | 90.84 |
yes | 22 | 5.30 | |
unknown | 16 | 3.86 | |
Stroke | no | 350 | 84.34 |
yes | 16 | 3.86 | |
unknown | 49 | 11.80 | |
Chronic kidney disease | no | 303 | 73.01 |
yes | 94 | 22.65 | |
unknown | 18 | 4.34 | |
Frailty index | <18.2 | 284 | 71.36 |
≥18.2 | 114 | 28.64 | |
Postoperative characteristics | |||
pT stage | T1a | 355 | 85.54 |
T1b | 50 | 12.05 | |
T2a | 6 | 1.45 | |
T2b | 0 | 0 | |
T3a | 4 | 0.96 | |
pN stage | N0 | 370 | 89.16 |
N1 | 8 | 1.93 | |
Nx | 37 | 8.92 | |
Grade | 1 | 145 | 34.94 |
2 | 206 | 49.64 | |
3 | 56 | 13.49 | |
4 | 8 | 1.93 | |
Surgical margin | negative | 367 | 88.43 |
positive | 48 | 11.57 | |
Tumor diameter | cm | 3.0 | 2.0–3.9 |
Histology type | clear cell | 323 | 77.83 |
papillary | 64 | 15.42 | |
chromophobe | 28 | 6.75 | |
Surgery and hospitalization | |||
Surgical treatment modality | Open | 215 | 51.81 |
Laparoscopic | 160 | 38.55 | |
Robotic | 40 | 9.64 | |
Warm ischemia time | ≥20 min | 129 | 31.08 |
<20 min | 286 | 68.92 | |
Length of stay | median | 7 | 5–9 |
Complications Clavien–Dindo scale | 0 | 194 | 48.74 |
1 | 164 | 39.52 | |
2 | 26 | 6.27 | |
3 | 10 | 2.41 | |
4 | 4 | 0.96 | |
unknown | 17 | 4.10 |
Renal Function | No. of Pts./Median | %/IQR | |
---|---|---|---|
Preoperative GFR CKD-EPI | mL/min | 88.33 | 70.98–100.80 |
Discharge GFR CKD-EPI | mL/min | 81.47 | 62.47–96.89 |
1-year GFR CKD-EPI | mL/min | 81.0 | 66.0–96.89 |
Significant GFR loss at discharge | no | 371 | 89.40 |
yes | 44 | 10.60 | |
Significant * GFR loss at 1 yr | no | 379 | 91.33 |
yes | 36 | 8.67 | |
CKD upstage ** at 1 yr | no | 292 | 70.36 |
yes | 123 | 29.64 | |
Oncological outcomes | |||
Recurrence | no | 331 | 79.76 |
yes | 82 | 19.76 | |
unknown | 2 | 0.48 | |
All-cause death | no | 364 | 87.71 |
yes | 49 | 11.81 | |
unknown | 2 | 0.48 | |
Cancer-specific death | no | 385 | 92.77 |
yes | 28 | 6.75 | |
unknown | 2 | 0.48 |
A. Univariable Analysis | ||||
Variable | Class | Estimate | Std. Error | p-value |
Age | −0.93 | 0.10 | <0.001 | |
Gender | male | ref | ||
female | 1.47 | 2.87 | 0.608 | |
Diabetes | no | ref | ||
yes | −9.93 | 4.07 | 0.015 | |
Myocardial Infarction | no | ref | ||
yes | −12.31 | 5.70 | 0.032 | |
Coronary Artery Disease | no | ref | ||
yes | −9.60 | 4.08 | 0.019 | |
Surgical Treatment Modality | open | ref | ||
laparoscopy | 11.19 | 2.68 | <0.001 | |
Frailty Index | <18.2 | ref | ||
≥18.2 | −10.59 | 2.07 | <0.001 | |
Preoperative Tumor Diameter On CT/MRI | cm | −2.49 | 1.00 | 0.014 |
Preoperative Chronic Kidney Disease | no | ref | ||
yes | −24.26 | 2.96 | <0.001 | |
Charlson Comorbidity Index | continuous | −7.29 | 0.68 | <0.001 |
Preoperative GFR CKD-EPI | mL/min | 0.85 | 0.04 | <0.001 |
Preoperative Creatinine | mg/dL | −26.07 | 1.92 | <0.001 |
Preoperative Hemoglobin | g/dL | 0.12 | 0.08 | 0.147 |
B. Multivariable analysis | ||||
Variable | Class | Estimate | Std. Error | p-value |
Intercept | 29.37 | 5.69 | <0.001 | |
Preoperative Tumor Diameter on CT/MRI | cm | −1.65 | 0.58 | 0.005 |
Preoperative GFR CKD-EPI | mL/min | 0.76 | 0.04 | <0.001 |
Charlson Comorbidity Index | continuous | −1.95 | 0.54 | 0.0004 |
Model | R2 | RMSE | MAE | Brier Score (MSE) | Calibration Slope | Calibration-in-the-Large |
---|---|---|---|---|---|---|
Linear Regression | 0.668 | 13.13 | 10.07 | 172.44 | 1.038 | −4.700 |
ANN | 0.679 | 12.92 | 10.15 | 166.88 | 1.076 | −7.588 |
XGBoost | 0.554 | 15.23 | 11.76 | 231.85 | 0.905 | 5.974 |
Random Forest | 0.643 | 13.63 | 10.58 | 185.69 | 1.025 | −3.736 |
SNV | 0.663 | 13.23 | 10.32 | 175.15 | 1.049 | −6.587 |
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Ślusarczyk, A.; Sharma, S.; Garbas, K.; Piekarczyk, H.; Zapała, P.; Shi, J.; Radziszewski, P.; Qu, L.; Zapała, Ł. Prediction of Glomerular Filtration Rate Following Partial Nephrectomy for Localized Renal Cell Carcinoma with Different Machine Learning Techniques. Cancers 2025, 17, 1647. https://doi.org/10.3390/cancers17101647
Ślusarczyk A, Sharma S, Garbas K, Piekarczyk H, Zapała P, Shi J, Radziszewski P, Qu L, Zapała Ł. Prediction of Glomerular Filtration Rate Following Partial Nephrectomy for Localized Renal Cell Carcinoma with Different Machine Learning Techniques. Cancers. 2025; 17(10):1647. https://doi.org/10.3390/cancers17101647
Chicago/Turabian StyleŚlusarczyk, Aleksander, Sumit Sharma, Karolina Garbas, Hanna Piekarczyk, Piotr Zapała, Jinhao Shi, Piotr Radziszewski, Le Qu, and Łukasz Zapała. 2025. "Prediction of Glomerular Filtration Rate Following Partial Nephrectomy for Localized Renal Cell Carcinoma with Different Machine Learning Techniques" Cancers 17, no. 10: 1647. https://doi.org/10.3390/cancers17101647
APA StyleŚlusarczyk, A., Sharma, S., Garbas, K., Piekarczyk, H., Zapała, P., Shi, J., Radziszewski, P., Qu, L., & Zapała, Ł. (2025). Prediction of Glomerular Filtration Rate Following Partial Nephrectomy for Localized Renal Cell Carcinoma with Different Machine Learning Techniques. Cancers, 17(10), 1647. https://doi.org/10.3390/cancers17101647