Machine Learning-Based Model Helps to Decide which Patients May Benefit from Pancreatoduodenectomy
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Population
2.2. Features and Data Analysis
2.3. Algorithms
2.3.1. Decision Tree
2.3.2. Logistic Regression
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Numerical Features | Categorical Features |
---|---|
Age at the time of Surgery | Sex |
Ca 19.9 at diagnosis (U/mL) | Pre-operative cholangitis (Yes/No) |
CEA at diagnosis (ng/mL) | Pre-operative biliary drainage (Yes/No) |
Total proteins at diagnosis (g/dL) | Neoadjuvant therapy (Yes/No) |
Albumin at diagnosis (g/dL) | AJCC-stage (8th edition) |
Total bilirubin at diagnosis/Jaundice (mg/dL) | Histological grade |
Lymphocyte count | ASA score |
Neutrophil count | Type of Surgery |
Number of total ICU (days) | Venous vascular resection (Yes/No) |
Image tumor size (mm) | Arterial vascular resection (Yes/No) |
Weight (kg) | Pancreas Consistency (Soft/Firm) |
Height (cm) | Wirsung Localization (Excentric/Concentric) |
Number of excised lymph nodes | Sealant (Yes/No) |
Number of metastasized lymph nodes | Hemorrhagic complication (Yes/No) |
Post-op ho-spitalization days | Respiratory infection (Yes/No) |
Date of recurrence | Degree of gastric stasis (A, B, C) |
Date of death | Surgical re-intervention (Yes/No) |
CEA in EUS aspirate (ng/mL) | ICU readmission (Yes/No) |
Amylase in EUS aspirate (U/L) | Re-hospitalization (Yes/No) |
Lymphocyte to Neutrophil ratio | Diagnosis |
Recurrence (Yes/No) | |
Status (Deceased/Alive) | |
Degree of the Pancreatic fistula | |
Clavien |
Features | Training (n = 172) | Holdout Dataset or Validation Set (n = 33) | p Value | |
---|---|---|---|---|
Age (years) | Median; SD | 66.38; 9.95 | 66.52; 10.4 | 0.9451 |
Gender | Female (%) Male (%) | 48 52 | 39 61 | 0.3525 |
Ca19-9 (U/mL) | Median; SD | 985; 3770 | 2273; 7881 | 0.1718 |
Jaundice | % | 80 | 90 | 0.1455 |
Neutrophils (*109/L) Pre-operative | Median; SD | 4631; 2203 | 4514; 2082 | 0.7786 |
Lymphocytes (*109/L) Pre-operative | Median; SD | 1889; 777.6 | 1540; 752.4 | 0.0187 |
Lymphocytes/ Neutrophils Pre-operative | Median; SD | 0.559; 0.682 | 0.379; 0.22 | 0.1338 |
Neoadjuvant Chemotherapy | Yes (%) No (%) | 11.6 88.4 | 9.2 90.8 | 0.6741 |
Nodule size (mm) | Median; SD | 30.83; 11.78 | 30.81; 14.36 | 0.9959 |
ASA | I (%) | 8.2 | 0.0 | 0.0001 |
II (%) | 65.3 | 22.2 | ||
III (%) | 25.5 | 77.8 | ||
IV (%) | 1.0 | 0.0 | ||
Sealant (Epiploplasty) | % | 69.7 | 75.7 | 0.8753 |
Target (Survivors or Non-survivors) | % | 71.5 | 72.7 | 0.8878 |
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Vigia, E.; Ramalhete, L.; Filipe, E.; Bicho, L.; Nobre, A.; Mira, P.; Macedo, M.; Aguiar, C.; Corado, S.; Chumbinho, B.; et al. Machine Learning-Based Model Helps to Decide which Patients May Benefit from Pancreatoduodenectomy. Onco 2023, 3, 175-188. https://doi.org/10.3390/onco3030013
Vigia E, Ramalhete L, Filipe E, Bicho L, Nobre A, Mira P, Macedo M, Aguiar C, Corado S, Chumbinho B, et al. Machine Learning-Based Model Helps to Decide which Patients May Benefit from Pancreatoduodenectomy. Onco. 2023; 3(3):175-188. https://doi.org/10.3390/onco3030013
Chicago/Turabian StyleVigia, Emanuel, Luís Ramalhete, Edite Filipe, Luís Bicho, Ana Nobre, Paulo Mira, Maria Macedo, Catarina Aguiar, Sofia Corado, Beatriz Chumbinho, and et al. 2023. "Machine Learning-Based Model Helps to Decide which Patients May Benefit from Pancreatoduodenectomy" Onco 3, no. 3: 175-188. https://doi.org/10.3390/onco3030013