Prediction models for biochemical failure after FS-HDR-BT 171 focal salvage treatment for radiorecurrent prostate cancer. While we did not investigate the predictive value of PSA nadir alone, we did incorporate it in our model by using PSA reduction. We argue that this might be a better predictor than PSA nadir, given its dependence on pre-salvage PSA. Furthermore, PSA nadir is also influenced by other factors, such as prostate volume.12 In another study by Peters et al.18, univariable analysis showed that age was associated with biochemical failure in 62 patients treated with whole-gland brachytherapy. Upon multivariable analysis, age was excluded.18 This is potentially explained by the limited sample size that was used. It could also be that age and DFSI are associated, as explained in the previous paragraph, and that the effect of age disappears when corrected for DFSI (or vice versa). However, due to the limited sample size we chose not to include DFSI as a candidate predictor. We did not assess pre-salvage Gleason score as a potential predictor, as biopsies were not performed from the end of 2017 onwards (leading to 45.4% missing values). Also, while some have identified variables from the primary tumour and/or treatment as predictors, we did not investigate any primary tumour characteristics because of our limited sample size and missing data in these characteristics. Furthermore, the predictive value of these variables in focal salvage studies seems limited.17 With an extended sample size and followup, we could potentially investigate the added value of some of these predictors. There are several strengths to our study. Missing data for candidate pre-salvage predictors was very low (0.7%) due to prospective data collection. The inclusion of patients treated off-protocol also makes the study sample more representative and increases external validity. Furthermore, candidate predictors for multivariable analysis were selected based on literature and clinical knowledge rather than by performing univariable analysis, thereby minimising the occurrence of type-I errors.29 The online dynamic nomograms we created are helpful tools to quickly assess and visualise individual predicted bDFS. The study has some limitations. First, external validation of this model is necessary. Several other focal salvage strategies have been described, all with minor differences with respect to eligibility of patients. Therefore, such cohorts offer an opportunity for external validation. Especially since both models use predictors that are known to be related to prostate cancer progression and none of them are treatment specific. External validation of our models could lead to adjustment of these models and thereby improve predictive accuracy and be applicable to other focal salvage modalities. Although evidence is still scarce and mainly limited to the primary treatment setting, fractionated salvage treatment (e.g. 2 x 13.5 Gy) might improve oncological outcomes in recurrent prostate cancer patients.35–37 Despite taking into account the sample size, some overfitting is indicated by the suboptimal shrinkage factors of 0.85 and 0.81, indicating 15% and 19% optimism, respectively. Furthermore, limiting the number of candidate variables might have led to missing important predictors, such as DFSI.17 Consequently, the C-statistic of 0.73 of the first model might be improved by including other potential predictors when sample size has increased. Third, length of follow-up was relatively short with a median of 25.1 months, thus the models perform optimal within a timeframe of approximately two years. Fourth, tumour volume was based on the delineated GTV. Although GTV delineation was based on mp-MRI and PSMA-PET/CT, which improves the estimation of tumour volume compared to mp-MRI alone38, interobserver variability due to the lack 8
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