Individualized assessment predictive models for risk and overall survival in elderly patients of primary kidney cancer with bone metastases: A large population-based study

Development and validation of a prediction model for the prognosis of renal cell carcinoma with liver metastases: a population-based cohort study

Background

Current studies on the establishment of prognostic model for renal cell carcinoma (RCC) with liver metastases (LM) were scarce. This study aimed to develop nomograms to predict the prognosis of RCC with LM.

Methods

Patients diagnosed with RCC between 2010 and 2021 from the Surveillance, Epidemiology, and End Results (SEER) database were selected. The eXtreme Gradient Boosting (XGBoost) and Random Forest (RF) machine learning algorithms were used to screen for the most influential factors affecting prognosis, and the Venn diagram method was employed for further refinement. Subsequently, a nomogram related to brain metastases was constructed. The performance of the nomograms was evaluated through receiver operating characteristics (ROC) curves, calibration plots, C-index, time-dependent C-index, and decision curve analysis (DCA). Kaplan-Meier (K-M) survival curves were used to provide additional verification of the clinical efficacy of the nomogram.

Results

This research comprised 2,395 RCC patients with LM. The Venn diagram demonstrated that age, histological type, grade, AJCC T stage, AJCC N stage, surgery, chemotherapy, marital status, and lung metastasis were highly relevant variables to patients with LM. The AUC, C-index, calibration curves, and DCA curves showed excellent performance of the nomogram. Additionally, the prognostic nomogram accurately classified RCC with LM patients into low- and high-risk groups for mortality.

Conclusion

This study developed a novel nomogram to predict the prognostic factors of RCC with LM, providing a valuable reference for making accurate clinical decisions.

A dynamic visualization clinical tool constructed and validated based on the SEER database for screening the optimal surgical candidates for bone metastasis in primary kidney cancer

The implementation of primary tumor resection (PTR) in the treatment of kidney cancer patients (KC) with bone metastases (BM) has been controversial. This study aims to construct the first tool that can accurately predict the likelihood of PTR benefit in KC patients with BM (KCBM) and select the optimal surgical candidates. This study acquired data on all patients diagnosed with KCBM during 2010-2015 from the Surveillance, Epidemiology, and End Results (SEER) database. Propensity score matching (PSM) was utilized to achieve balanced matching of PTR and non-PTR groups to eliminate selection bias and confounding factors. The median overall survival (OS) of the non-PTR group was used as the threshold to categorize the PTR group into PTR-beneficial and PTR-Nonbeneficial subgroups. Kaplan-Meier (K-M) survival analysis was used for comparison of survival differences and median OS between groups. Risk factors associated with PTR-beneficial were identified using univariate and multivariate logistic regression analyses. Receiver operating characteristic (ROC), area under the curve (AUC), calibration curves, and decision curve analysis (DCA) were used to validate the predictive performance and clinical utility of the nomogram. Ultimately, 1963 KCBM patients meeting screening criteria were recruited. Of these, 962 patients received PTR and the remaining 1061 patients did not receive PTR. After 1:1 PSM, there were 308 patients in both PTR and non-PTR groups. The K-M survival analysis results showed noteworthy survival disparities between PTR and non-PTR groups, both before and after PSM (p < 0.001). In the logistic regression results of the PTR group, histological type, T/N stage and lung metastasis were shown to be independent risk factors associated with PTR-beneficial. The web-based nomogram allows clinicians to enter risk variables directly and quickly obtain PTR beneficial probabilities. The validation results showed the excellent predictive performance and clinical utility of the nomograms for accurate screening of optimal surgical candidates for KCBM. This study constructed an easy-to-use nomogram based on conventional clinicopathologic variables to accurately select the optimal surgical candidates for KCBM patients.

The novel dynamic nomogram and risk classification system constructed for predicting post-surgical overall survival and mortality risk in primary chondrosarcoma: a population study based on SEER database

BACKGROUND

Surgery is the predominant method to improve the prognosis of primary chondrosarcoma patients. We aimed to construct the first reliable nomogram to predict the post-surgical overall survival (OS) of primary chondrosarcoma patients.

METHODS

We downloaded all primary chondrosarcoma patients treated with surgery between 2004 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database, and randomized them into training set (60%) and validation set (40%). Cox proportional regression analysis was applied to the training set to identify independent prognostic variables, and then constructed a nomogram for predicting 3-, 5-, and 8-year OS. The Harrell's concordance index (C-index), receiver operating characteristic curve (ROC), the area under curve (AUC), calibration curve and decision curve analysis (DCA) was used to assess the predictive efficacy and clinical applicability of the nomogram. The nomogram was also compared with The American Joint Committee on Cancer (AJCC) staging system.

RESULTS

A total of 1005 post-surgical primary chondrosarcoma patients were included in this study. We finally identified five independent prognostic variables to construct the nomogram, being age, grade, tumor size, disease stage and histological type. The C-index results showed that the prediction performance of the nomogram was significantly better than the AJCC staging system. In the training set, (C-index: 0.805, 95% CI 0.879-0.730 vs 0.686, 95% CI 0.606-0.766); in the validation set, (C-index: 0.811, 95% CI 0.895-0.727 vs 0.697, 95% CI 0.647-0.799). Additionally, the AUC values generated by the ROC were all greater than 0.8, which also indicated the excellent predictive performance of the nomogram. The calibration curves showed that the predicted survival rate was highly similar to the actual. Time-dependent ROC and DCA showed that the nomogram has better predictive performance and net clinical benefits than the AJCC staging system. Finally, a risk stratification system based on nomogram was constructed.

CONCLUSION

We successfully constructed and validated the first nomogram that could reliably predict 3-, 5-, and 8-year post-surgical OS in primary chondrosarcoma patients. Furthermore, the web-based dynamic nomogram could be more conveniently applied to clinic, providing assistance to surgeons and patients.