Enhancing Preoperative Outcome Prediction: A Comparative Retrospective Case–Control Study on Machine Learning versus the International Esodata Study Group Risk Model for Predicting 90-Day Mortality in Oncologic Esophagectomy
Abstract
:Simple Summary
Abstract
1. Introduction
2. Materials and Methods
2.1. Setting and Study Population
2.2. IESG Risk Model Validation
2.3. ML Training and Validation
2.4. Feature Importance Analysis
2.5. Comparison between IESG Model and ML Classifiers
3. Results
3.1. Patient Baseline Characteristics
3.2. IESG Risk Model Evaluation
3.3. Machine Learning Model Evaluation
3.4. Feature Importance
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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Characteristic | Total n = (552) | Training Cohort n = (409) | Validation Cohort n = (143) | p Value |
---|---|---|---|---|
Age | 0.55 | |||
≤40 (%) | 9 (1.6) | 8 (2.0) | 1 (0.7) | |
41–50 (%) | 42 (7.6) | 33 (8.1) | 9 (6.3) | |
51–60 (%) | 152 (27.5) | 118 (28.9) | 34 (23.8) | |
61–70 (%) | 178 (32.2) | 130 (31.8) | 48 (33.6) | |
71–80 (%) | 149 (27.0) | 105 (25.7) | 44 (30.8) | |
>80 (%) | 22 (4.0) | 15 (3.7) | 7 (4.9) | |
Mean (SD) | 64.3 (10.1) | 63.8 (10.1) | 65.8 (9.9) | |
BMI | 0.82 | |||
<18.5 (%) | 22 (4.0) | 17 (4.2) | 5 (3.5) | |
18.5–24.9 (%) | 216 (39.1) | 156 (38.1) | 60 (42.0) | |
25–29.9 (%) | 240 (43.5) | 182 (44.5) | 58 (40.6) | |
≥30 (%) | 74 (13.4) | 54 (13.2) | 20 (14.0) | |
Mean (SD) | 25.8 (4.4) | 25.8 (4.5) | 25.7 (4.3) | |
Sex | 0.44 | |||
Male (%) | 439 (79.5) | 329 (80.4) | 110 (76.9) | |
Female (%) | 113 (20.5) | 80 (19.6) | 33 (23.1) | |
ECOG | 0.22 | |||
0 (%) | 354 (64.1) | 255 (62.3) | 99 (69.2) | |
1 (%) | 186 (33.7) | 146 (35.7) | 40 (28.0) | |
2 (%) | 12 (2.2) | 8 (2.0) | 4 (2.8) | |
Histology | 0.07 | |||
AC (%) | 360 (65.2) | 267 (65.3) | 93 (65.0) | |
SCC (%) | 177 (32.1) | 128 (31.3) | 49 (34.4) | |
Other (%) | 14 (2.5) | 14 (3.4) | 0 (0.) | |
Neoadjuvant treatment | 0.3 | |||
Chemotherapy (%) | 273 (49.5) | 212 (51.8) | 61 (42.7) | |
Radiochemotherapy (%) | 183 (33.2) | 136 (33.3) | 47 (32.9) | |
Radiotherapy alone (%) | 2 (0.4) | 2 (0.5) | 0 (0.0) | |
Tpre | 0.25 | |||
T0 (%) | 1 (0.2) | 1 (0.2) | 0 (0.0) | |
T1 (%) | 42 (7.6) | 30 (7.3) | 12 (8.4) | |
T2 (%) | 67 (12.1) | 54 (13.2) | 13 (9.1) | |
T3 (%) | 300 (54.3) | 246 (60.1) | 54 (37.8) | |
T4 (%) | 23 (4.2) | 19 (4.6) | 4 (2.8) | |
Tis (%) | 1 (0.2) | 1 (0.2) | 0 (0.0) | |
Tx (%) | 8 (1.4) | 4 (1.0) | 4 (2.8) | |
Npre | <0.001 | |||
N0 (%) | 124 (22.5) | 96 (23.5) | 28 (19.6) | |
N1 (%) | 168 (30.4) | 129 (31.5) | 39 (27.3) | |
N2 (%) | 74 (13.4) | 73 (17.8) | 1 (0.7) | |
N3 (%) | 26 (4.7) | 24 (5.9) | 2 (1.4) | |
Nx (%) | 10 (1.8) | 4 (1.0) | 6 (4.2) | |
Comorbidities | ||||
CCI mean (SD) | 7.4 (3.8) | 7.3 (3.8) | 7.6 (3.8) | 0.46 |
Myocardial infarction (%) | 19 (3.4) | 13 (3.2) | 6 (4.2) | 0.76 |
Peripheral vascular disease (%) | 24 (4.3) | 13 (3.2) | 11 (7.7) | 0.04 |
Chronic pulmonary disease (%) | 125 (22.6) | 98 (24.0) | 27 (18.9) | 0.26 |
Peptic ulcer disease (%) | 11 (2.0) | 6 (1.5) | 5 (3.5) | 0.25 |
Liver disease mild (%) | 32 (5.8) | 19 (4.6) | 13 (9.1) | 0.08 |
Diabetes without chronic complications (%) | 84 (15.2) | 59 (14.4) | 25 (17.5) | 0.46 |
Hemiplegia or paraplegia (%) | 12 (2.2) | 7 (1.7) | 5 (3.5) | 0.35 |
Liver disease moderate/severe (%) | 3 (0.5) | 3 (0.7) | 0 (0.0) | 0.71 |
Renal disease (%) | 2 (0.4) | 2 (0.5) | 0 (0.0) | 0.98 |
Metastatic solid tumor (%) | 154 (27.9) | 115 (28.1) | 39 (27.3) | 0.93 |
30-day mortality (%) | 15 (2.7) | 10 (2.4) | 5 (3.5) | 0.71 |
90-day mortality (%) | 32 (5.8) | 19 (4.6) | 13 (9.1) | 0.08 |
Classifier | AUROC Mean, 95% CI (Low–High) | AUPRC Mean, 95% CI (Low–High) | MCC Mean, 95% CI (Low–High) | |
---|---|---|---|---|
90-day mortality | Decision tree | 0.52 (0.51–0.53) | 0.21 (0.17–0.24) | 0.07 (0.05–0.09) |
Gradient boosting | 0.64 (0.63–0.65) | 0.16 (0.15–0.16) | 0.12 (0.10–0.14) | |
Linear support vector machine | 0.51 (0.50–0.52) | 0.10 (0.10–0.10) | 0.01 (0.00–0.01) | |
Logistic regression | 0.64 (0.63–0.64) | 0.20 (0.19–0.21) | 0.26 (0.25–0.28) | |
Neural network | 0.61 (0.60–0.63) | 0.25 (0.24–0.27) | 0.24 (0.21–0.27) | |
Random forest | 0.64 (0.63–0.65) | 0.21 (0.20–0.22) | 0.27 (0.25–0.28) | |
Support vector machine | 0.62 (0.61–0,63) | 0.23 (0.21–0.25) | 0.21 (0.18–0.24) | |
IESG score stratification | 0.44 (0.32–0.56) | 0.11 (0.05–0.21) | 0.15 (0.03–0.27) | |
30-day mortality | Decision tree | 0.49 (0.47–0.50) | 0.17 (0.14–0.22) | 0.03 (0.02–0.05) |
Gradient boosting | 0.58 (0.56–0.60) | 0.06 (0.06–0.07) | 0.03 (0.01–0.05) | |
Linear support vector machine | 0.50 (0.49–0.51) | 0.04 (0.04–0.05) | 0.00 (0.00–0.00) | |
Logistic regression | 0.45 (0.44–0.45) | 0.03 (0.03–0.04) | 0.04 (0.03–0.05) | |
Neural network | 0.44 (0.41–0.46) | 0.07 (0.06–0.09) | 0.06 (0.03–0.08) | |
Random forest | 0.47 (0.45–0.49) | 0.05 (0.03–0.06) | 0.07 (0.05–0.08) | |
Support vector machine | 0.52 (0.51–0.54) | 0.08 (0.07–0.09) | 0.03 (0.01–0.04) |
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Winter, A.; van de Water, R.P.; Pfitzner, B.; Ibach, M.; Riepe, C.; Ahlborn, R.; Faraj, L.; Krenzien, F.; Dobrindt, E.M.; Raakow, J.; et al. Enhancing Preoperative Outcome Prediction: A Comparative Retrospective Case–Control Study on Machine Learning versus the International Esodata Study Group Risk Model for Predicting 90-Day Mortality in Oncologic Esophagectomy. Cancers 2024, 16, 3000. https://doi.org/10.3390/cancers16173000
Winter A, van de Water RP, Pfitzner B, Ibach M, Riepe C, Ahlborn R, Faraj L, Krenzien F, Dobrindt EM, Raakow J, et al. Enhancing Preoperative Outcome Prediction: A Comparative Retrospective Case–Control Study on Machine Learning versus the International Esodata Study Group Risk Model for Predicting 90-Day Mortality in Oncologic Esophagectomy. Cancers. 2024; 16(17):3000. https://doi.org/10.3390/cancers16173000
Chicago/Turabian StyleWinter, Axel, Robin P. van de Water, Bjarne Pfitzner, Marius Ibach, Christoph Riepe, Robert Ahlborn, Lara Faraj, Felix Krenzien, Eva M. Dobrindt, Jonas Raakow, and et al. 2024. "Enhancing Preoperative Outcome Prediction: A Comparative Retrospective Case–Control Study on Machine Learning versus the International Esodata Study Group Risk Model for Predicting 90-Day Mortality in Oncologic Esophagectomy" Cancers 16, no. 17: 3000. https://doi.org/10.3390/cancers16173000