Objective: The International Society of Urological Pathology (ISUP) has proposed a grading system to classify renal cell carcinoma (RCC). However, classification using biopsy specimens remains problematic and, consequently, the accuracy of a biopsy-based diagnosis is relatively poor. This study aims to combine clinical and immunohistochemical (IHC) factors for the prediction of high ISUP grade clear cell RCC (ccRCC) in an attempt to complement and improve the accuracy of a biopsy-based diagnosis. Methods: A total of 362 ccRCC patients were enrolled in this study and used for the training set. We performed IHC analysis of 18 protein markers on standard tissue sections using an automated stainer. Multivariate logistic regression models were developed to evaluate independent predictors for high ISUP grade. We evaluated different prediction models using receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) analysis. A nomogram for the derivation of an integrated score for predicting high ISUP grade ccRCC and a calibration curve were also plotted. Finally, an internal validation cohort was examined to evaluate the performance of our integrated scoring system and nomogram. Results: Multivariate logistic analyses revealed seven credible candidates for predicting high grade ISUP. These were age, tumor diameter, surgery, and CK7, Ki-67, PTEN, and MTOR protein expression. The ROC curves for the clinical, IHC and integrated models were compared in the training set, and the AUC for each was 0.731, 0.744, and 0.801, respectively. DeLong's test showed that the integrated model was significantly better at predicting high ISUP grade, when compared with the other models. Internal validation confirmed the good performance of the integrated score in predicting ISUP grade. Conclusion: We have developed a nomogram integrating clinical and immunohistochemical parameters to predict high ISUP grade for M0 ccRCC patients. This nomogram may offer potentially useful information during preoperative individualized patient risk assessment, and consequently may help urologists when planning personalized management regimens.
Frontiers in oncology. 2018 Dec 18*** epublish ***
Junlong Wu, Wen-Hao Xu, Yu Wei, Yuan-Yuan Qu, Hai-Liang Zhang, Ding-Wei Ye
Department of Urology, Fudan University Shanghai Cancer Center, Shanghai, China.