Enhancing personalized prognosis in appendiceal (AC) and cecal adenocarcinoma (CA) with machine learning.

e16484 Background: AA and CA are rare gastrointestinal neoplasms (2% and 1.2%, respectively) that are understudied and difficult to diagnose. We aimed to apply machine learning (ML) to identify prognostic factors and to enhance survival outcomes. 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: In total, 136,010 patients were included. Of these, 120,000 had CA and 16,010 had AC. Most patients (56%) were female and 80.3% were White. The median patient age was 69 years, and the median tumor size was 4.4 cm. Most patients were T1 (54.2%) and T4 was rare (16.5%). Nodal involvement was observed in 14.7% of cases, while M0 accounted for 91.7%. Of the metastatic cases, 1.2% were in the liver and 0.2% in the liver and lungs. Among the patients, 99.4% underwent surgery, 0.5% received radiation, and 15.7% received chemotherapy. The 5-year overall survival (OS) was higher in patients aged < 69 years (68.8%) than in those aged ≥ 69 years (48.4%). Survival also varied by primary site, with appendix tumors showing a significantly higher OS (82.1%) than cecal tumors (55.7%). Metastatic status strongly influenced OS, with M0 having a much higher rate (67.5%) than M1 (10.9%). Similarly, nodal involvement impacted outcomes, as N0 patients had the highest OS (72.7%), followed by N1 (49.6%), and N2 (25.5%). Multivariate Cox regression analysis identified older age, male sex, radiation, metastasis, and White and Black race as poor prognostic factors, whereas surgery and chemotherapy were good prognostic factors. Gradient boosting and Random Forest were the most accurate models. The ML models identified TNM stage as the most significant prognostic factor, followed by age and primary site. The performance metrics for all ML algorithms are summarized in Table. Conclusions: ML plays a crucial role in personalized medicine by identifying the key prognostic factors in AC and CA. Further research is needed to assess the benefits of radiation therapy for managing these rare malignancies. ML Model Accuracy Precision Recall F1 score AUC LR 64.9% 62.2% 38% 47.1% 0.716 KNN 61.9% 52.7% 74.2% 61.6% 0.664 RFC 65.1% 57.6% 58.1% 57.8% 0.722 GBC 65% 57.9% 55.% 56.4% 0.719 MLP 64% 57.5% 57.3% 57.4% 0.719