Predictive factors for disease recurrence in patients with locally advanced renal cell carcinoma treated with curative surgery

Prediction of Fuhrman nuclear grade for clear cell renal carcinoma by a multi-information fusion model that incorporates CT-based features of tumor and serum tumor associated material

PURPOSE

Prediction of Fuhrman nuclear grade is crucial for making informed herapeutic decisions in clear cell renal cell carcinoma (ccRCC). The current study aimed to develop a multi-information fusion model utilizing computed tomography (CT)-based features of tumors and preoperative biochemical parameters to predict the Fuhrman nuclear grade of ccRCC in a non-invasive manner.

METHODS

218 ccRCC patients confirmed by histopathology were retrospectively analyzed. Univariate and multivariate logistic regression analyses were performed to identify independent predictors and establish a model for predicting the Fuhrman grade in ccRCC. The predictive performance of the model was evaluated using receiver operating characteristic (ROC) curves, calibration, the 10-fold cross-validation method, bootstrapping, the Hosmer-Lemeshow test, and decision curve analysis (DCA).

RESULTS

R.E.N.A.L. Nephrometry Score (RNS) and serum tumor associated material (TAM) were identified as independent predictors for Fuhrman grade of ccRCC through multivariate logistic regression. The areas under the ROC curve (AUC) for the multi-information fusion model composed of the above two factors was 0.810, higher than that of the RNS (AUC 0.694) or TAM (AUC 0.764) alone. The calibration curve and Hosmer-Lemeshow test showed the integrated model had a good fitting degree. The 10-fold cross-validation method (AUC 0.806) and bootstrap test (AUC 0.811) showed the good stability of the model. DCA demonstrated that the model had superior clinical utility.

CONCLUSION

A multi-information fusion model based on CT features of tumor and routine biochemical indicators, can predict the Fuhrman grade of ccRCC using a non-invasive approach. This model holds promise for assisting clinicians in devising personalized management strategies.

A meta-analysis of the platelet-lymphocyte ratio: A notable prognostic factor in renal cell carcinoma

BACKGROUND

The platelet-lymphocyte ratio (PLR) has been assessed in some studies on renal cell carcinoma (RCC), but the results have been inconsistent. This meta-analysis aims to review and report the latest data regarding the prognostic role of the PLR in RCC patients.

METHOD

Articles were searched in the PubMed, EMBASE, and Cochrane Library electronic databases. Studies were filtered according to a selection strategy, and data corresponding to the index of interest were extracted. A fixed-effects model or random-effects model was selected based on heterogeneity. The sensitivity analysis was carried out by eliminating the studies one by one. Finally, funnel plots and Egger's test were used to assess publication bias, and the trim and fill method was used to assess the impact of bias on the results.

RESULTS

In total, 15,193 patients with RCC from 44 studies were included in this meta-analysis. The pooled analysis indicated that the higher the PLR was, the poorer the prognosis for RCC patients in terms of overall survival (hazard ratio (HR) = 1.01 (95% confidence interval (CI) 1.00, 1.02), P = 0.010), cancer-special survival (CSS) (HR = 1.21 (95% CI 1.00, 1.46), P = 0.05), progression-free survival (HR = 1.44 (95% CI 1.28, 1.62), P < 0.00001), recurrence-free survival (HR = 1.73 (95% CI 1.11, 2.71), P = 0.02), disease-free survival (HR = 1.63 (95% CI 0.91, 2.94), P = 0.01) and metastasis-free survival (HR = 1.223 (95% CI 0.712, 2.099), P = 0.466). In the subgroup analysis of high PLR, targeted treatment, TKI use, nivolumab use, surgical treatment, clear cell RCC, metastasis, Asian race, and high PLR were related to poor prognosis.

CONCLUSION

This study showed that a high PLR was associated with the poor prognosis of RCC patients, but more studies are needed to confirm the value of the PLR.