Salvage radiotherapy strategy and its prognostic significance for patients with locoregional recurrent cervical cancer after radical hysterectomy: a multicenter retrospective 10-year analysis

Mediation Effects of Self-care Self-efficacy and Health-promoting Behaviors on Fear of Cancer Recurrence and Posttraumatic Growth in Postoperative Patients With Cervical Cancer: A Cross-sectional Study

PURPOSE

High recurrence risks significantly contribute to poor health outcomes among postoperative cervical cancer patients. This study aimed to determine the impact of fear of cancer recurrence (FCR) on the posttraumatic growth (PTG) in postoperative cervical cancer patients and to investigate the mediating effects of self-care self-efficacy and health-promoting behaviors within this relationship.

METHODS

A total of 334 cervical cancer patients who had undergone surgery in three tertiary hospitals in Liaoning Province, China, were recruited using a convenience sampling method. Data were collected using the general information questionnaire, the Fear of Progression Questionnaire (FCR), the Post-traumatic Growth Inventory (PTG), the Strategies Used by People to Promote Health (self-care self-efficacy), and the Health-Promoting Lifestyle Profile (health-promoting behaviors). Data analysis was performed using descriptive analysis, Pearson's correlations, and multiple linear regression analysis. A structural equation model was conducted using Amos 24.0 software.

RESULTS

PTG of cervical cancer patients after surgery was significantly and negatively associated with FCR (r = -.54, p < .001), while positively correlated with self-care self-efficacy (r = .51, p < .001) and health-promoting behaviors (r = .59, p < .001). The mediation model revealed that self-care self-efficacy (Boots 95% CI 0.39 ∼ 0.15) and health-promoting behaviors (Boots 95% CI 0.51 ∼ 0.24) independently served as mediating factors, respectively. Self-care self-efficacy and health-promoting behaviors played a significant chain mediating effect between FCR and PTG (Boots 95% CI 0.19 ∼ 0.07), with an indirect effect of 11.6%.

CONCLUSION

This study demonstrated that self-care self-efficacy and health-promoting behaviors partially mediated the relationship between FCR and PTG in cervical cancer survivors after surgery. Implementing tailored interventions that focus on enhancing self-care self-efficacy and health-promoting behaviors among cervical cancer survivors may help alleviate concerns about cervical cancer recurrence and enhance PTG following surgery.

PD-1 Inhibitors Combined with Tyrosine Kinase Inhibitors with or without Hepatic Artery Infusion Chemotherapy for the First-Line Treatment of HBV-Related Advanced Hepatocellular Carcinoma: A Retrospective Study

Purpose

Comparing the efficacy and safety of programmed cell death protein-1 (PD-1) inhibitors combined with tyrosine kinase inhibitors (TKIs) with or without hepatic artery infusion chemotherapy (HAIC) in HBV-related advanced HCC and exploring prognostic predictors of the combined regimen.

Patients and Methods

A total of 194 patients diagnosed with HBV-related advanced HCC between 2020 and 2022 were included in the study, including 99 in the HAIC combined with PD-1 inhibitors plus TKIs (HPT group) and 95 in the PD-1 inhibitors plus TKIs (PT group). The efficacy was evaluated according to the tumor response rate and survival, and the safety was evaluated according to the adverse events.

Results

The HPT group showed higher overall response rate and disease control rate than the PT group. The median overall survival (OS) of the HPT group and the PT group were 18.10 months and 12.57 months, respectively, and the difference was statistically significant (hazard ratio (HR) = 0.519, 95% confidence interval (CI): 0.374-0.722, P < 0.001). The median progression-free survival (PFS) was 9.20 months in the HPT group and 6.33 months in the PT group (HR = 0.632, 95% CI: 0.470-0.851, P = 0.002). In addition, albumin bilirubin (ALBI) and systemic inflammatory response index (SIRI) are independent prognostic factors affecting HAIC combined with targeted immunotherapy and can be used as prognostic predictors. Almost all patients included in the study experienced treatment-related adverse events (TRAEs) of varying degrees of severity, with grade 1-2 adverse events predominating.

Conclusion

The HPT group had better OS and PFS than the PT group in patients with HBV-related advanced HCC. In addition, high ALBI and high SIRI were associated with poor prognosis in the HAIC combined group.

Interpretable machine learning model based on the systemic inflammation response index and ultrasound features can predict central lymph node metastasis in cN0T1-T2 papillary thyroid carcinoma

Background

It is arguable whether individuals with T1-T2 papillary thyroid cancer (PTC) who have a clinically negative (cN0) diagnosis should undergo prophylactic central lymph node dissection (pCLND) on a routine basis. Many inflammatory indices, including the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), and systemic immune-inflammatory index (SII), have been reported in PTC. However, the associations between the systemic inflammation response index (SIRI) and the risk of central lymph node metastasis (CLNM) remain unclear.

Methods

Retrospective research involving 1,394 individuals with cN0T1-T2 PTC was carried out, and the included patients were randomly allocated into training (70%) and testing (30%) subgroups. The preoperative inflammatory indices and ultrasound (US) features were used to train the models. To assess the forecasting factors as well as drawing nomograms, the least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression were utilized. Then eight interpretable models based on machine learning (ML) algorithms were constructed, including decision tree (DT), K-nearest neighbor (KNN), support vector machine (SVM), artificial neural network (ANN), random forest (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost). The performance of the models was evaluated by incorporating the area under the precision-recall curve (auPR) and the area under the receiver operating characteristic curve (auROC), as well as other conventional metrics. The interpretability of the optimum model was illustrated via the shapley additive explanations (SHAP) approach.

Results

Younger age, larger tumor size, capsular invasion, location (lower and isthmus), unclear margin, microcalcifications, color Doppler flow imaging (CDFI) blood flow, and higher SIRI (≥0.77) were independent positive predictors of CLNM, whereas female sex and Hashimoto thyroiditis were independent negative predictors, and nomograms were subsequently constructed. Taking into account both the auROC and auPR, the RF algorithm showed the best performance, and superiority to XGBoost, CatBoost and ANN. In addition, the role of key variables was visualized in the SHAP plot.

Conclusions

An interpretable ML model based on the SIRI and US features can be used to predict CLNM in individuals with cN0T1-T2 PTC.