Added Value of Systemic Inflammation Markers in Predicting Clinical Stage T1 Renal Cell Carcinoma Pathologically Upstaged to T3a

Gross Hematuria Does not Affect the Selection of Nephrectomy Types for Clinical Stage 1 Clear Cell Renal Cell Carcinoma: A Multicenter, Retrospective Cohort Study

PURPOSE

This study aimed to discuss the correlation between gross hematuria and postoperative upstaging (from T1 to T3a) in patients with cT1 clear cell renal cell carcinoma (ccRCC) and to compare oncologic outcomes of partial nephrectomy (PN) and radical nephrectomy (RN) in patients with gross hematuria.

METHODS

A total of 2145 patients who met the criteria were enrolled in the study (including 363 patients with gross hematuria). The least absolute selection and shrinkage operator logistic regression was used to evaluate the risk factor of postoperative pathological upstaging. The propensity score matching (PSM) and stable inverse probability of treatment weighting (IPTW) analysis were used to balance the confounding factors. The Kaplan-Meier analysis and multivariate Cox proportional risk regression model were used to assess the prognosis.

RESULTS

Gross hematuria was a risk factor of postoperative pathological upstaging (odds ratio [OR] = 3.96; 95% confidence interval [CI] 2.44-6.42; P < 0.001). After PSM and stable IPTW adjustment, the characteristics were similar in corresponding patients in the PN and RN groups. In the PSM cohort, PN did not have a statistically significant impact on recurrence-free survival (hazard ratio [HR] = 1.48; 95% CI 0.25-8.88; P = 0.67), metastasis-free survival (HR = 1.24; 95% CI 0.33-4.66; P = 0.75), and overall survival (HR = 1.46; 95% CI 0.31-6.73; P = 0.63) compared with RN. The results were confirmed in sensitivity analyses.

CONCLUSIONS

Although gross hematuria was associated with postoperative pathological upstaging in patients with cT1 ccRCC, PN should still be the preferred treatment for such patients.

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.

Factors associated with pathological tumor stage and T3a upstage in clinical T1-2 renal cell carcinoma

INTRODUCTION

Considering the oncological outcomes, understanding the preoperative factors associated with and predicting advanced stage and T3a upstage will help in risk assessment and selection of the right treatment.

MATERIAL AND METHOD

Patients with postoperative pathology of Renal Cell Carcinoma (RCC) and stage T1-2 N0M0 were included in the study. Demographic and pathological characteristics of the patients, Neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), and De Ritis- the ratio of aspartate aminotransferase (AST) and alanine aminotransferase (ALT) were recorded. Patients were classified according to T stage (T1-2 vs T3-4) and T3a upstage (T3a upstaged vs non-T3a upstaged).

RESULTS

A total of 289 patients participated in the study when inclusion and exclusion criteria were applied. No difference was found between the groups in terms of age, gender, body mass index, laterality, ABO blood group, Rh positivity and comorbidities. According to multivariate analysis, PLR, AST/ALT, Fuhrman grade, open radical nephrectomy (RN) and Clear Cell pathological subtype were found to be significant-independent factors in predicting advanced stage (T3-4) and T3a upstage (P<0.05).

CONCLUSION

It was found that higher PLR and AST/ALT ratios were associated with more advanced stage and postoperative T3a upstage in RCC patients. In addition, these patients more frequently had open RN and had higher Fuhrman grades, while the clear cell subtype was less common.

A morphology-based nephrometry score to predict pathological upstaging to T3 renal cell carcinoma

Background

Patients with clinical T1-2 renal cell carcinoma (RCC) upstaging to pathological T3 showed worse survival prognosis than those without upstaging. We aimed to develop and validate a morphology-based nephrometry scoring system for predicting pathological upstaging to T3 of RCC.

Methods

We retrospectively reviewed 200 patients with clinical T1-2 RCC who underwent surgical treatment. The nephrometry scores were measured through preoperative computed tomography images. The risk factors of pathological upstaging were identified by logistic regression models. The predictive accuracy of a novel morphology-based nephrometry scoring system (M-Index), was compared with R.E.N.A.L (radius, exophytic/endophytic, nearness, anterior/posterior, location), PADUA (preoperative aspects and dimensions used for an anatomic classification), DAP (diameter, axial, polar) and C-Index scores.

Results

The upstaging rate of the population was 17% (34 out of 200 patients). The upstaging and non-upstaging groups were comparable in terms of age, gender ratio, body mass index, tumor laterality, and pathological type, while the upstaging group tended to have large tumor diameter, irregular tumor morphology, inner tumor location, and short polar and axial distance. Large tumor diameter refers to larger than 5 cm, while irregular tumor morphology refers to not regular shapes such as round, oval, or lobular. Univariate and multivariate logistic regression analyses showed that tumor morphology [odds ratio (OR) 3.26, 95% confidence interval (CI): 1.79-5.97] and tumor rim location (OR 2.95, 95% CI: 1.16-7.46) were independent risk factors for pathological upstaging. The receiver operating characteristic curve and decision curve analysis (DCA) demonstrated the novel M-Index based on tumor morphology and rim location outperformed R.E.N.A.L, PADUA, DAP, and C-Index in the prediction of pathological upstaging (area under curve 0.756 vs. 0.728 vs. 0.641 vs. 0.661 vs. 0.743).

Conclusions

Consisting of fewer non-complex parameters, the M-Index is an intuitive and practical tool with satisfactory predictive power for pathological upstaging to T3 in RCC patients undergoing surgery.

A novel nomogram can predict pathological T3a upstaged from clinical T1a in localized renal cell carcinoma

Hypothesis:

Nomogram can be built to predict the pathological T3a upstaging from clinical T1a in patients with localized renal cell carcinoma before surgery.

Purpose:

Renal cell carcinoma (RCC) patients with clinical T1a (cT1a) disease who are upstaged to pathological T3a (pT3a) have reduced survivals after partial nephrectomy. We aimed to develop a nomogram-based model predicting pT3a upstaging in RCC patients with preoperative cT1a based on multiple preoperative blood indexes and oncological characteristics.

Materials and Methods:

Between 2010 and 2019, 510 patients with cT1a RCC were individually matched according to pT3a upstaging and pathological T1a (pT1a) at a 1:4 ratio using clinicopathologic features. Least absolute shrinkage and selection operator regression analysis was used to identify the most important risk factor from 40 peripheral blood indicators, and a predictive model was established. Multivariate logistic regression analysis was performed with the screened blood parameters and clinical data to identify significant variables. Harrell’s concordance index (C-index) was applied to evaluate the accuracy of the model for predicting pT3a upstaging in patients with cT1a RCC.

Results:

Out of 40 blood indexes, the top ranked predictor was fibrinogen (FIB). Age, the ratio of the tumor maximum and minimum diameter (ROD), FIB, and tumor size were all independent risk factors for pT3a upstaging in multivariate analysis. A predictive ARFS model (Age, ROD, FIB, tumor Size) was established, and the C-index was 0.756 (95% CI, 0.681-0.831) and 0.712 (95% CI, 0.638-0.785) in the training and validation cohorts, respectively.

Conclusions:

Older age, higher ROD, increased FIB level, and larger tumor size were independent risk factors for upstaging. The ARFS model has a high prediction efficiency for pT3a upstaging in patients with cT1a RCC.

Integrative Analysis of Peripheral Blood Indices for the Renal Sinus Invasion Prediction of T1 Renal Cell Carcinoma: An Ensemble Study Using Machine Learning-Assisted Decision-Support Models

Purpose

Renal sinus invasion is an attributive factor affecting the prognosis of renal cell carcinoma (RCC). This study aimed to construct a risk prediction model that could stratify patients with RCC and predict renal sinus invasion with the help of a machine learning (ML) algorithm.

Patients and Methods

We retrospectively recruited 1229 patients diagnosed with T1 stage RCC at the Baotou Cancer Hospital between November 2013 and August 2021. Iterative analysis was used to screen out predictors related to renal sinus invasion, after which ML-based models were developed to predict renal sinus invasion in patients with T1 stage RCC. The receiver operating characteristic curve (ROC), decision curve analysis (DCA), and clinical impact curve (CIC) were performed to evaluate the robustness and clinical practicability of each model.

Results

A total of 21 candidate variables were shortlisted for model building. Iterative analysis screened that neutrophil to albumin ratio (NAR), hemoglobin level * albumin level * lymphocyte count/platelet count ratio (HALP), prognostic nutrition index (PNI), body mass index*serum albumin/neutrophil-lymphocyte ratio (AKI), NAR, and fibrinogen (FIB) concentration (NARFIB), platelet to lymphocyte ratio (PLR), and R.E.N.A.L score was related to renal sinus invasion and contributed significantly to ML-based algorithm. The areas under the ROC curve (AUCs) of the random forest classifier (RFC) model, support vector machine (SVM), eXtreme gradient boosting (XGBoost), artificial neural network (ANN), and decision tree (DT) ranged from 0.797 to 0.924. The optimal risk probability of renal sinus invasion predicted was RFC (AUC = 0.924, 95% confidence interval [CI]: 0.414–1.434), which showed robust discrimination for identifying high-risk patients.

Conclusion

We successfully develop practical models for renal sinus invasion prediction, particularly the RFC, which could contribute to early detection via integrating systemic inflammatory factors and nutritional parameters.