Abstract
4552 Background: Decisions to start imaging pts with PCa can be difficult due to the heterogeneous nature of the disease. Nomograms can combine information on multiple disease factors to improve predictive accuracy and are not influenced by subjective confounders that may affect clinical judgment. However, currently available nomograms for PCa predict current or future risk of positive BS in ADT-naive pts only. We have developed a new nomogram to predict current BS positivity in ADT-treated pts with PCa using a very large pt sample. Methods: All BS records from 1650 ADT-treated pts who received treatment at the Memorial Sloan-Kettering Cancer Center from 2000 to 2011 were analyzed. Pts were followed up from the start of ADT until the first positive BS or last hospital visit. Multivariate logistic regression analysis was used to model the variables that could be used for predicting likelihood of metastases and the variables were incorporated into the nomogram model. The current probability of bone metastasis was generated by the nomogram using the pt variables at each BS and the predictive accuracy was quantified by calculating the concordance index (C-index). Results: In total, 3949 BS records were analyzed and 950 pts had a positive scan. There was an average of 1.1 prior negative scans before a positive result was recorded. Median prostate-specific antigen (PSA) was 2.5 ng/mL and mean PSA doubling time (PSADT) was 7.8 months. At the same time of the positive scans, 49.4% of pts had a PSA <10 ng/mL. Based on clinical relevance and data availability, 13 variables were included in the nomogram model: number of previous negative BSs, PSA, PSADT, prostatectomy, radiotherapy, brachytherapy, chemotherapy, immunotherapy, estrogen therapy, cryotherapy, clinical trial enrollment, and most recent primary and secondary Gleason grades. C-index for the nomogram was 0.76. Conclusions: This is the first nomogram to predict current BS positivity in pts with PCa following ADT. The nomogram was devised from a very large data set from a single center and provides high predictive accuracy.
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