An Integrated Score and Nomogram Combining Clinical and Immunohistochemistry Factors to Predict High ISUP Grade Clear Cell Renal Cell Carcinoma

Abstract

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.

Keywords: ISUP grade; clear cell renal cell carcinoma; immunohistochemistry; prediction model; renal tumor biopsy.

Figure 1

The representative images of CK7 (A,B), Ki-67 (C,D), PTEN (E,F), MTOR (G,H), and HIF-1α (I,J) positive and negative IHC staining of tumor tissues are shown in 100x standard microscopic enlargement.

Figure 2

Waterfall plot of different models was contrasted in clinical factors (A), IHC markers (C) and integrated penal (E), with horizontal axis representing the patients and vertical axis the score. ROC curve were performed to validate low or high ISUP classification from based on the three logit models. The shadow part represent confidential interval and AUC index in clinical, IHC and integrated indicators was 0.731, 0.744, and 0.801 in (B,D,F), respectively.

Figure 3

(A) Nomogram of integrated score for predicting high ISUP grade. The total points were conducted by summarizing the points for each variable. High grade risk was determined by specific total points at the bottom of plotting scale. (B) The calibration curve was closely consistent with ideal diagonal curve (P < 0.05), indicating that this nomogram was in high precision.

Figure 4

Waterfall plot (A) and ROC curve (B) were constructed to validate predicting performance based on integrated score and nomogram in internal validation set (121 patients). AUC index is 0.791.