Nomograms in Robot-Assisted Radical Prostatectomy Predicting Extracapsular Extension and Lymph Node Involvement

Background: Several preoperative tools have been developed during the last two decades to assist patients and physicians across the decision processes in the diagnosis, staging and treatment of prostate cancer (PCa). In the current chapter, we aimed to describe currently available tools predicting adverse pathological features at final surgical specimen. Main body of the chapter: In patients who are candidate to radical prostatectomy, predictive models should be routinely used to assess the risk of adverse pathological features at surgical specimen. This is particularly true when considering the identification of lymph node invasion. As for biopsy, the introduction magnetic resonance imaging (MRI) has remarkably improved the ability to optimize the indication for lymph node dissection during radical prostatectomy. Moreover, the upcoming inclusion of information derived from Prostate-Specific Membrane Antigen (PSMA) PET scan may further improve the prediction of nodal involvement. On the other hand, the use of preoperative risk tools assessing the risk of adverse pathological features such as extracapsular extension or seminal vesicles invasion is also well accepted in routine clinical practice. In this context, the use of nomograms facilitates preoperative counselling and the decision-making process; particularly, the integration of clinical and MRI-derived parameters can improve the ability to select proper candidates for nerve-sparing approaches, or for alternative treatments such as active surveillance or focal therapies. Conclusion: Overall, preoperative nomograms for PCa developed during the last two decades exhibited good calibration characteristics and accuracy to predict adverse pathological findings at final pathology. Most of these tools exhibited elevated accuracy, good calibration characteristics and promising clinical net benefit, when they were validated. In the upcoming years, the integration of these clinical tools with information derived from genomic classifiers or PSMA PET results will further improve their predictive ability.