Hui Ouyang

The prognostic significance of sex in follicular thyroid carcinoma (FTC) remains controversial. This study aimed to clarify the prognostic significance of

Current guidelines recommend total thyroidectomy for all T3b differentiated thyroid carcinoma (DTC) with gross strap muscle invasion, yet evidence supporting this universal approach remains limited and conflicting. We analyzed 6,920 T3b DTC patients from the SEER database (2004–2022) who underwent lobectomy (n = 282) or total thyroidectomy (n = 6,638). Overall survival (OS) served as the primary outcome. Random survival forest (RSF) with SHapley Additive exPlanations (SHAP) analysis identified interactions between surgical approach and clinical variables. Cox regression with restricted cubic splines examined age-dependent surgical effects on OS. Neither RSF nor Cox regression (HR 0.96, 95% CI: 0.68–1.37, P = 0.84) showed a significant OS difference between surgical approaches. SHAP interaction analysis revealed significant age-dependent heterogeneity. Restricted cubic splines identified age 45 as the threshold where the surgical benefit converged. In patients <45 years, total thyroidectomy demonstrated significantly superior survival outcomes compared with lobectomy (adjusted HR 3.26, 95% CI: 1.30–8.22, P = 0.012; 10-year survival 95.5 vs 98.4%). Conversely, no difference existed in patients ≥45 years (HR 0.85, 95% CI: 0.58–1.24, P = 0.40). The multiplicative interaction was 0.26 (95% CI: 0.10–0.70, P = 0.008), confirming a negative age–surgery interaction. Although this study demonstrates that surgical approach shows no significant survival difference in T3b DTC, the negative interaction effect between surgical approach and age indicates that the benefit of total thyroidectomy progressively diminishes with advancing age, providing a survival advantage only in patients younger than 45 years. These findings challenge universal total thyroidectomy recommendations and support age-stratified surgical decision-making for T3b DTC.

Background: High-grade serous ovarian cancer (HGSOC) remains one of the most challenging gynecological malignancies, with over 70% of ovarian cancer patients ultimately experiencing disease progression. The current prognostic tools for progression-free survival (PFS) in HGSOC patients have limitations. This study aims to develop an explainable machine learning (ML) model for predicting PFS in HGSOC patients. Methods: Nine ML algorithms for PFS prediction were developed using a prospective cohort of 310 HGSOC patients consecutively enrolled from a large Chinese tertiary hospital between January 2017 and December 2020. The optimal model was internally validated using the 1000 bootstrap method. The SHapley Additive exPlanations (SHAP) method was employed to interpret the model in terms of feature importance and feature effects. The final model, constructed with the optimal feature subset, was deployed as an interactive web-based Shiny app. Results: The random survival forest (RSF) model demonstrated superior predictive performance compared to other ML models, the RFS model constructed with an optimal feature subset in the optimal imputed dataset achieved a superior 1000 bootstrap C-index of 0.755 (95% CI: 0.750–0.780) and a Brier score of 0.183 (95% CI: 0.175–0.190). SHAP analysis identified tumor residual, HE4, FIGO stage, T stage, CA125, age, ascites volume, platelet counts, and BMI as the top nine contributing factors. It also revealed potential nonlinear relationships and important thresholds between HE4, CA125, age, ascites volume, platelet counts, the body mass index, and PFS risk. Additionally, interaction effects were found between tumor residual and age, HE4, and CA125. Finally, an interactive web-based Shiny app for the model was developed and accessible at https://rsfmodels.shinyapps.io/ocRSF/. Conclusion: An explainable ML model for PFS prediction in HGSOC patients was developed with superior results. The publicly accessible web tool based on the optimized model facilitates its utility in clinical settings, potentially improving individualized patient management and treatment decision-making in HGSOC.

Background: Infected pancreatic necrosis (IPN) is a severe complication of acute pancreatitis, with mortality rates ranging from 15 to 35%. However, limited studies exist to predict the survival of IPN patients and nomogram has never been built. This study aimed to identify predictors of mortality, estimate conditional survival (CS), and develop a CS nomogram and logistic regression nomogram for real-time prediction of survival in IPN patients. Methods: A prospective cohort study was performed in 335 IPN patients consecutively enrolled at a large Chinese tertiary hospital from January 2011 to December 2022. The random survival forest method was first employed to identify the most significant predictors and capture clinically relevant nonlinear threshold effects. Instantaneous death risk and CS was first utilized to reveal the dynamic changes in the survival of IPN patients. A Cox model-based nomogram incorporating CS and a logistic regression-based nomogram were first developed and internally validated with a bootstrap method. Results: The random survival forest model identified seven foremost predictors of mortality, including the number of organ failures, duration of organ failure, age, time from onset to first intervention, hemorrhage, bloodstream infection, and severity classification. Duration of organ failure and time from onset to first intervention showed distinct thresholds and nonlinear relationships with mortality. Instantaneous death risk reduced progressively within the first 30 days, and CS analysis indicated gradual improvement in real-time survival since diagnosis, with 90-day survival rates gradually increasing from 0.778 to 0.838, 0.881, 0.974, and 0.992 after surviving 15, 30, 45, 60, and 75 days, respectively. After further variables selection using step regression, five predictors (age, number of organ failures, hemorrhage, time from onset to first intervention, and bloodstream infection) were utilized to construct both the CS nomogram and logistic regression nomogram, both of which demonstrated excellent performance with 1000 bootstrap. Conclusion: Number of organ failures, duration of organ failure, age, time from onset to first intervention, hemorrhage, bloodstream infection, and severity classification were the most crucial predictors of mortality of IPN patients. The CS nomogram and logistic regression nomogram constructed by these predictors could help clinicians to predict real-time survival and optimize clinical decisions.