Development and validation of a scoring model for predicting surgical benefit in patients with Masaoka stage III/IV thymic carcinomas.

Background: Thymomas and thymic carcinomas are rare and aggressive thymic tumors that are usually detected in advanced stages. Surgery is the mainstay of treatment; however, the role of surgery in advanced disease is controversial due to factors such as myasthenia gravis; thus, decisions about whether to perform surgical interventions are complex. Further studies need to be conducted to explore the potential benefits of surgery in the treatment of advanced thymic tumors. This study proposed a predictive surgical decision score (SDS) model to optimize patient prognosis by identifying the patients likely to benefit most from surgery. Methods: The study retrospectively analyzed the data of 1,207 patients with Masaoka stage III/IV thymic carcinomas from the Surveillance, Epidemiology and End Results (SEER) database and clinical records from The First Affiliated Hospital of Guangzhou Medical University. We assessed clinical factors including age, gender, tumor differentiation grade, tumor size, tumor-node-metastasis (TNM) stage, and metastasis locations. Surgical benefits were evaluated using propensity score matching (PSM) analysis to compare overall survival (OS) between the surgical and non-surgical groups. A Cox regression model was employed to identify independent prognostic factors. Kaplan-Meier curves were used to further analyze surgical benefits across different subgroups. Furthermore, we developed an SDS model, which was subjected to both internal and external validation to evaluate its accuracy and discriminative capacity in predicting the benefits of surgical intervention. Results: In the SEER database cohort, 1,106 eligible patients were identified, with 61.8% undergoing surgery, resulting in a propensity score-matched cohort of 474 patients. Surgical resection was found to be an independent favorable prognostic indicator in advanced-stage thymus malignancies [hazard ratio (HR): 0.45, 95% confidence interval (CI): 0.34-0.58]. The optimal SDS model, which included histological subtype, grade of differentiation, tumor size, T stage, nodal involvement, and distant metastasis, had an Akaike information criterion (AIC) value of 816.382. SDS values ranged from -115 to 313 points. The internal validation cohort consisted of 186 males and 161 females, with 60.5% undergoing surgery, whereas the external cohort included 55 males and 46 females, with 65.3% receiving surgical intervention. The receiver operating characteristic (ROC) curve analysis of the SDS model revealed satisfactory predictive accuracy on both internal and external validation [area under the curve (AUC): 0.80, 95% CI: 0.75-0.84; and AUC: 0.73, 95% CI: 0.64-0.83, respectively]. Patients with high SDS values undergoing surgery exhibited superior survival compared to those with low SDS values not undergoing surgery (P<0.05). Conclusions: Surgical resection was independently associated with improved survival outcomes in patients with advanced-stage thymic malignancies. Additionally, we successfully developed an SDS prediction model to enhance the selection process for optimal surgical candidates, underscoring its potential clinical implications in guiding treatment decisions.