BACKGROUND: This study aimed to create an algorithm using the decision tree method to classify patients with suspected acute cholecystitis into those who may improve with medical treatment, those who should undergo surgery for acute cholecystitis, and those with complicated cholecystitis, using laboratory parameters alone. METHODS: A total of 1,352 patients treated for acute cholecystitis at our hospital over four years were retrospectively analyzed. Patients were divided into groups based on whether they received medical treatment or surgery. Various demographic and laboratory parameters were recorded. A decision tree algorithm was used to classify patients based on these parameters. Statistical analyses were performed using SPSS, and the decision tree's performance was evaluated with 10-fold cross-validation. An additional decision tree was created for gangrenous cholecystitis using the same methods. RESULTS: The decision tree identified the platelet-to-lymphocyte ratio (PLR) as the most critical parameter for distinguishing between patients requiring surgery and those suitable for conservative treatment. The algorithm demonstrated an 82.17% diagnostic accuracy for predicting operative need and a 73.86% accuracy for identifying gangrenous cholecystitis. C-reactive protein (CRP) levels, platelet (PLT) values, white blood cell (WBC) counts, and patient age were also significant factors in the decision-making process. The CONCLUSION: The decision tree algorithm effectively differentiates between uncomplicated and complicated cholecystitis using easily obtainable laboratory parameters. This method offers a cost-effective, rapid alternative to imaging studies, facilitating timely and appropriate treatment decisions, ultimately improving patient outcomes and reducing healthcare costs.

Background and Objective: The metastatic lymph node ratio (MLNR) has emerged as a significant prognostic marker in colon cancer (CC). This study evaluated the impact of MLNR on disease-free survival (DFS), overall survival (OS), and recurrence, comparing it to TNM staging. Materials and Methods:A retrospective analysis included CC patients who underwent surgical treatment. The prognostic value of MLNR was analyzed using Kaplan-Meier survival analysis and multivariate regression, alongside clinical factors such as age, tumor localization, and type of surgery. The relationship between MLNR and the efficacy of adjuvant chemotherapy was also investigated. Results:Patients with an MLNR > 0.125 showed a significantly higher risk of disease recurrence (hazard ratio [HR]: 7.0, p< 0.001) and shorter DFS. Kaplan-Meier analysis demonstrated that lower MLNR was associated with significantly longer DFS (p< 0.001). MLNR was found to be an independent prognostic factor, outperforming TNLC in predicting OS and DFS (p< 0.001). Patients receiving adjuvant therapy had a significantly lower risk of disease recurrence compared to those who did not (p=0.015). Multivariate analysis identified adjuvant therapy and MLNR as independent prognostic factors. The reduced recurrence risk observed in patients receiving adjuvant therapy (Exp(B): 0.234, p=0.038) underscores the efficacy of this treatment. Emergency surgery was significantly associated with worse survival (p=0.023). Conclusion:MLNR is a valuable prognostic marker in colon cancer and can complement the TNM staging system to refine survival predictions.Our findings suggest that MLNR can guide adjuvant treatment decisions, supporting more personalized therapeutic approaches.Larger prospective studies are needed to validate the optimal MLNR cut-off value and further assess its clinical utility.