Published February 24, 2016 | Version v1
Journal article Open

Beyond D’Amico risk classes for predicting recurrence after external beam radiotherapy for prostate cancer: the Candiolo classifier

  • 1. Neuroscience Department, Human Physiology Section, University of Torino, corso Raffaello 30, 10125, Torino, Italy
  • 2. Division of Radiation Oncology, European Institute of Oncology, and University of Milan, Milan, Italy
  • 3. Division of Radiation Oncology, Maggiore University Hospital of Novara, Novara, Italy
  • 4. Division of Radiation Oncology, FPO-IRCCS Cancer Center of Candiolo (Torino), Candiolo, Italy
  • 5. Division of Radiation Oncology, Cardinal Massaia Hospital, Asti, Italy
  • 6. Division of Radiation Oncology, degli infermi Hospital, Biella, Italy
  • 7. Division of Radiation Oncology, Civile Hospital, Ivrea, Italy

Description

Background: The aim of this work is to develop an algorithm to predict recurrence in prostate cancer patients treated with radical radiotherapy, getting up to a prognostic power higher than traditional D’Amico risk classification.

Methods: Two thousand four hundred ninety-three men belonging to the EUREKA-2 retrospective multi-centric database on prostate cancer and treated with external-beam radiotherapy as primary treatment comprised the study population. A Cox regression time to PSA failure analysis was performed in univariate and multivariate settings, evaluating the predictive ability of age, pre-treatment PSA, clinical-radiological staging, Gleason score and percentage of positive cores at biopsy (%PC). The accuracy of this model was checked with bootstrapping statistics. Subgroups for all the variables’ combinations were combined to classify patients into five different “Candiolo” risk-classes for biochemical Progression Free Survival (bPFS); thereafter, they were also applied to clinical PFS (cPFS), systemic PFS (sPFS) and Prostate Cancer Specific Survival (PCSS), and compared to D’Amico risk grouping performances.

Results: The Candiolo classifier splits patients in 5 risk-groups with the following 10-years bPFS, cPFS, sPFS and PCSS: for very-low-risk 90 %, 94 %, 100 % and 100 %; for low-risk 74 %, 88 %, 94 % and 98 %; for intermediate-risk 60 %, 82 %, 91 % and 92 %; for high-risk 43 %, 55 %, 80 % and 89 % and for very-high-risk 14 %, 38 %, 56 % and 70 %. Our classifier outperforms D’Amico risk classes for all the end-points evaluated, with concordance indexes of 71.5 %, 75.5 %, 80 % and 80.5 % versus 63 %, 65.5 %, 69.5 % and 69 %, respectively.

Conclusions: Our classification tool, combining five clinical and easily available parameters, seems to better stratify patients in predicting prostate cancer recurrence after radiotherapy compared to the traditional D’Amico risk classes.

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Additional details

Funding

CHIC – Computational Horizons In Cancer (CHIC): Developing Meta- and Hyper-Multiscale Models and Repositories for In Silico Oncology 600841
European Commission