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Predictive Models in Prostate Cancer

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Robot-Assisted Radical Prostatectomy

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Abstract

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: (1) presence of PCa at biopsy; (2) adverse pathological features at final surgical specimen; (3) oncological outcomes after radical treatment.

Main body of the chapter: Risk calculators have been developed to estimate individual risk of PCa in men enrolled in PSA screening programs and the subsequent indication for prostate biopsy based on multiple factors. However, their use is not recommended due to suboptimal accuracy and the lack of prospective validation. The integration of predictive tools with MRI-derived parameters will allow for optimizing the indication for prostate biopsy. 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 indication, the introduction MRI has remarkably improved the ability to optimize the indication for lymph node dissection during radical prostatectomy. The use of preoperative risk tools assessing the risk of disease progression after treatment is also well accepted in routine clinical practice; in this context, risk stratification classes or scores are generally preferred to nomograms due to their straightforward format, which facilitates preoperative counselling and the decision-making process without compromising predictive accuracy.

Conclusion: Overall, prediction models for PCa developed during the last two decades exhibited good calibration characteristics and accuracy to predict the PCa at a screening level, adverse pathological findings at final pathology and oncological outcomes after radical treatment. In the upcoming years, the integration of the clinical tools with information derived from genomic classifiers or MRI results will substantially improve their predictive ability.

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Acknowledgements

Disclosure: The authors declare no conflict of interest.

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This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Elio Mazzone certifies that all conflicts of interest, including specific financial interests and relationships and affiliations relevant to the subject matter or materials discussed in the manuscript (e.g., employment/affiliation, grants or funding, consultancies, honoraria, stock ownership or options, expert testimony, royalties, or patents filed, received, or pending), are the following: None.

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Authors and Affiliations

  1. Division of Oncology/Unit of Urology, URI, IRCCS Ospedale San Raffaele, Milan, Italy

    Elio Mazzone, Giorgio Gandaglia, Vito Cucchiara & Alberto Briganti

  2. Vita-Salute San Raffaele University, Milan, Italy

    Elio Mazzone, Giorgio Gandaglia, Vito Cucchiara & Alberto Briganti

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  1. Elio Mazzone
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  2. Giorgio Gandaglia
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  3. Vito Cucchiara
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  4. Alberto Briganti
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Correspondence to Elio Mazzone .

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Editors and Affiliations

  1. Changzheng Hospital, Shanghai, China

    Shancheng Ren

  2. Institute of Urology, University College London, London, UK

    Senthil Nathan

  3. Urology Clinic, Medical, Surgical and Health Sciences Department, University of Trieste, Trieste, Italy

    Nicola Pavan

  4. The First affiliated Hospital of Guangzhou Medical University, Guangzhou, China

    Di Gu

  5. Urology, University College London Hospital NHS Trust, Hatfield, UK

    Ashwin Sridhar

  6. Department of Urology, Rush University Medical Center, Chicago, IL, USA

    Riccardo Autorino

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Mazzone, E., Gandaglia, G., Cucchiara, V., Briganti, A. (2022). Predictive Models in Prostate Cancer. In: Ren, S., Nathan, S., Pavan, N., Gu, D., Sridhar, A., Autorino, R. (eds) Robot-Assisted Radical Prostatectomy. Springer, Cham. https://doi.org/10.1007/978-3-031-05855-4_26

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  • DOI: https://doi.org/10.1007/978-3-031-05855-4_26

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