Identifying key prognostic indicators in Wilms tumor using machine learning techniques.

4557 Background: Wilms tumor is a rare pediatric malignancy, accounting for 6% of pediatric tumors and primarily affecting the kidneys. Its impact on quality of life and long-term outcomes complicates management. This study leveraged machine learning (ML) to identify prognostic factors with the aim of enhancing prognosis and survival rates. Methods: Data were obtained from the SEER database (2004-2021). Patients who met any of the following criteria were excluded: diagnosis not confirmed by histology, previous history of cancer or other concurrent malignancies, or unknown data. To identify prognostic variables, we conducted Cox regression analysis and constructed prognostic models using ML algorithms to predict the 5-year survival. Patient records were randomly divided into training (70%) and validation (30%) sets. A validation method incorporating the area under the curve (AUC) of the receiver operating characteristic curve was used to validate the accuracy and reliability of the ML models. We also investigated the role of multiple therapeutic options using Kaplan-Meier survival analysis. Results: A total of 4,935 children were included. Among them, 47.72% underwent surgery, radiation, and chemotherapy; 45.07% underwent surgery and chemotherapy; and 7.21% underwent surgery alone. Most patients (53.3%) were females, 75% were white, followed by black (16.3%). The mean patient age was 3 years, and the mean tumor size was 10.6 cm. Most tumors were left-sided (51.1%) and 79.8% had no metastasis. The lungs were the most frequent site of metastasis (11%), followed by the liver and lungs at the same time (1.2%), and bone involvement was rare (0.6%). Radical surgery was the most common surgical approach (76.1%), followed by nephrectomy (4.9%). Patients who underwent surgery and chemotherapy had the highest 5-year OS (96.9%) and CSS (96.9%) compared to those who underwent surgery alone (OS: 95.5%, CSS: 95.5%) or surgery with chemotherapy and radiation (OS: 92.9%, CSS: 93.2%). Asian/Pacific Islander and white patients exhibited better OS (94.3% and 95.1%, respectively) than black patients (91.9%). Multivariate Cox regression analysis identified a large tumor size and older age as poor prognostic factors. Gradient boosting and MLP classifiers were the most accurate models. The ML models identified race as the most significant prognostic factor, followed by the TNM stage and age. The performance metrics for all ML algorithms are summarized in Table. Conclusions: This is the first study to apply ML to Wilms tumor, effectively identifying key prognostic factors. ML models show promise in enhancing survival predictions, potentially informing personalized treatment strategies, and improving patient outcomes. ML Model Accuracy Precision Recall F1 score AUC LR 59.5% 59.5% 99.6% 74.5% 0.573 KNN 56.1% 61.7% 68.7% 65.05% 0.572 RFC 59.9% 61.9% 84.4% 71.4% 0.596 GBC 60.7% 60.5% 97.3% 74.6% 0.582 MLP 60.9% 61.6% 90.5% 73.3% 0.589