## 4 Discussion  
Based on a systematic review and meta-analysis to compare the efficacies of RT and RP, [PERSON] et al. showed better overall survival after RP-based treatment and better BCR-free survival after external beam RT-based treatment. However, there was no significant difference in CSM between the two treatments [7]. The current guidelines do not provide clear recommendations regarding the selection criteria for high-risk PCa patients for RT or RP [6]. Previously, we showed that CIRT combined with long-term ADT for high-risk PCa patients showed relatively favorable outcomes compared with those after RT and RP in previous studies [14]. Also, compared with another study population [19], our high-risk PCa patients showed better BCR-free survival overall. However, a number of conventional high-risk PCa patients show poor outcomes after any definitive treatment, because high-risk PCa is thought to consist of a broad mix of diseases [13; 14]. Although the NCCN and D'Amico risk classifications are very simple and easy to utilize in daily clinical practice, clinicians would like to identify high-risk PCa patients using a more precise predictive model. Therefore, several prognostic models for PCa were developed  
Figure 3: Kaplan–Meier survival curves stratified by risk group, defined by calculated cut-off values. BCR: biochemical recurrence. and validated to evaluate certain endpoints, such as CSM and metastasis, using a very large database [29; 30]. The Candidio nomogram was overall more successful in predicting BCR in patients treated with RT combined with or without ADT compared with the D'Amico risk classification [21; 22]. Although we previously showed that reclassification of high-risk PCa patients after CIRT and ADT using the Candidio nomogram was better than that using the NCCN classification for predicting BCR [13], the Candidio nomogram was originally developed based on very-low- to very-high-risk PCa patients treated with conventional RT, not CIRT. Therefore, in the present study, we aimed to more precisely re-evaluate high-risk prostate cancer patients identified by conventional classification systems and to develop a novel nomogram. Our developed nomogram was very useful in predicting BCR-free survival after CIRT combined with ADT to reduce ADT duration in high-risk PCa patients with a relatively low-risk of recurrence and to prescribe more intensive treatments for patients with a very high risk of recurrence. Generally, predictive models incorporating more variables tend to have higher prediction accuracy. However, including too many unique variables can render a model impractical for routine clinical use. Although the factors included in our novel nomograms partially overlapped with those in previous reports (i.e., PSA level, cT stage, and biopsy GS) [5; 6; 21], our nomograms additionally incorporated age and %PC in addition to the variables included in simpler models. Our nomograms exhibited significant differences compared to other predictive models in the distribution of constituent predictors. As our nomograms are based on data obtained from high-risk PCa patients, highly aggressive predictors such as PSA level \(\geq\) 50 ng/mL, cT3b, and biopsy GS \(\geq\) 9 were assigned greater weights (Figure 2). Despite the efficacy of CIRT combined with ADT in controlling the majority of high-risk PCa patients, those with more severe and aggressive forms of the disease may be susceptible to BCR after CIRT and ADT. The contributions of age, PSA level, cT stage, and biopsy GS differed between the 5-year and 7-year nomograms. This divergence stemmed from the LASSO regression model, which selects predictors and assigns variable weights based on their specific predictive power within the designated time frame. Although both nomograms share fundamental predictors, the statistical significance and weighting of each variable vary due to differences in underlying survival dynamics and the influence of time-dependent factors on BCR-free survival.