Development of an Indian nomogram for predicting extracapsular extension in prostate cancer

The aim of our study was to develop a new Indian nomogram to estimate pathologic extracapsular extension (ECE) risk in prostate cancer, by including PI-RADS v1-based magnetic resonance imaging (MRI) ECE risk score to the clinical variables used in the Partin nomogram (PN).

We analyzed 273 patients who underwent MRI of prostate and radical prostatectomy (RP). Univariate and multivariate logistic regression analyses were performed to identify predictors of ECE. We calculated the area under the receiver operating characteristic curve (AUC) for three variables used in PN and MRI ECE risk score, and a new nomogram was designed using binary logistic regression. Calibration curves assessed the agreement between the actual ECE risk and the predicted probability of the new nomogram.

Out of 273 patients, 123 patients (45.1) had ECE on MRI, whereas 136 patients (49.8) had ECE on final pathology. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of MRI for predicting ECE were 76.6, 66.9, 70.0, 73.9, and 71.7 (confidence interval 95), respectively. Multivariate logistic regression analyses showed that clinical T-stage (cT), Gleason score (GS), and MRI ECE risk score remained significant. The highest and the lowest values of the AUC for single variables were 0.748 (MRI ECE risk score) and 0.636 (cT stage), respectively, and AUC for PN was 0.67. New nomogram designed using R statistical package has higher predictive accuracy (0.826) compared to PN (0.67) and good calibration.

MRI adds incremental value to PN. A new Indian nomogram can help in the decision-making process of nerve-sparing RP. This nomogram should be used with caution as validation is pending and will require further studies.