## 4 Discussion  
We developed a PCO classifier application to inform patients electing to undergo prostate cancer treatment with SBRT of potential outcomes. We can draw several conclusions based on our findings. The PCO classifier application outperforms the D'Amico and Kattan nomogram definitions for the evaluated endpoint of BCR at 5 years post-treatment. We compared recurrence risk analysis and observed c-indexes of 0.75 (training set) and 0.67 (validation set) for the PCO classifier application, 0.62 (training set) and 0.63 (validation set) for Kattan nomogram, and 0.64 (training set) and 0.64 (validation set) for D'Amico. The c-index ranges from 0.5 (equivalent to equal change) to 1.0 (perfect ability to discriminate) [10]. In general, the range of c-index values for prostate cancer is approximately 0.65-0.85 [11]. The PCO classifier application had a higher prognostic power than the other classifications examined. Accordingly, the PCO classifier application is suitable for predicting BCR in patients undergoing SBRT (Table4). The PCO classifier application divides patients into three risk-groups based on total PCO classifier scores, ranging from 0 to 156 points. The 5-year BCR-free survival rates for our sample were as follows: 98.3% for the low-risk group, 95.4% for the intermediate-risk group, and 86.4% for the high-risk group. Previous investigations based on risk groups receiving SBRT reported 5-year BCR-free survival rates of the following: 93%-97% for low-risk groups, 86.2%-92% for intermediate-risk groups, and 74.1-80% for high-risk groups [6; 16; 27]. Based on the Kattan nomogram definition, the reported 5-year BCR-free survival rate was 95% for low risk, 90% for the intermediate risk, and 80% for high risk [27]. Our 5-year BCR-free survival rates are specific for prostate cancer patients treated with SBRT, while the others include patients treated with radical prostatectomy, external beam radiation therapy, and interstitial radiation therapy [9; 10; 11; 12]. To generate a PCO classifier score, the PCO classifier application uses age at diagnosis, which is differently reported depending on the study. Age at diagnosis has been reported to predict BCR for prostate cancer [2; 28]. Some classifiers, such as the D'Amico and Kattan nomogram recurrence risk groups, do not use age to predict BCR. D'Amico and Kattan nomogram use PSA, Gleason scores, and clinical stage as predictors of outcomes [9; 10]. Others, such as the CAPRA score and the Candido classifier, use age as a predictor of outcomes. For the PCO classifier application, we decided to use age at diagnosis as a predictor of BCR based on one physician's comments. In addition, we observed that using this variable increased the predictive power of the PCO classifier application. In our results, age at diagnosis did correlate with biochemical outcomes after SBRT using the CyberKnife. The PCO classifier application employs a modified risk level--a combination of pretreatment PSA and Gleason score--to increase the predictive power of the model. The modified risk level was revised for the PCO classifier application based on [PERSON]'s (2013) risk assessment [16]. While the modified risk level is not statistically significant in our results at a \(p\)-value of 0.05, the relationship is in the direction we predicted, and the effect size is substantial, providing an interesting finding. Thus, the modified risk level is able to segregate based on established risk assessment criteria, and it increased the prognostic power of the PCO classifier application. We identify several potential limitations in our work. First, personalized medicine and molecular imaging are emerging approaches to improve the accuracy of traditional prediction methods. These could provide patients with a more personalized risk assessment of clinically relevant outcomes. Future research needs to use diverse factors and variables to predict treatment outcomes.