ORCID Profile
0000-0002-7849-603X
Current Organisation
Fondazione IRCCS Istituto Nazionale dei Tumori
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Publisher: Elsevier BV
Date: 04-2023
Publisher: Elsevier BV
Date: 06-2020
Publisher: Elsevier BV
Date: 11-2019
DOI: 10.1016/J.EURURO.2019.07.041
Abstract: Men with prostate cancer (PCa) on active surveillance (AS) are followed through regular prostate biopsies, a burdensome and often unnecessary intervention, not without risks. Identifying men with at a low risk of disease reclassification may help reduce the number of biopsies. To assess the external validity of two Canary Prostate Active Surveillance Study Risk Calculators (PASS-RCs), which estimate the probability of reclassification (Gleason grade ≥7 with or without >34% of biopsy cores positive for PCa) on a surveillance biopsy, using a mix of months since last biopsy, age, body mass index, prostate-specific antigen, prostate volume, number of prior negative biopsies, and percentage (or ratio) of positive cores on last biopsy. We used data up to November 2017 from the Movember Foundation's Global Action Plan (GAP3) consortium, a global collaboration between AS studies. External validity of the PASS-RCs for estimating reclassification on biopsy was assessed by calibration, discrimination, and decision curve analyses. Five validation cohorts (Prostate Cancer Research International: Active Surveillance, Johns Hopkins, Toronto, Memorial Sloan Kettering Cancer Center, and University of California San Francisco), comprising 5105 men on AS, were eligible for analysis. The in idual cohorts comprised 429-2416 men, with a median follow-up between 36 and 84 mo, in both community and academic practices mainly from western countries. Abilities of the PASS-RCs to discriminate between men with and without reclassification on biopsy were reasonably good (area under the receiver operating characteristic curve values 0.68 and 0.65). The PASS-RCs were moderately well calibrated, and had a greater net benefit than most default strategies between a predicted 10% and 30% risk of reclassification. Both PASS-RCs improved the balance between detecting reclassification and performing surveillance biopsies by reducing unnecessary biopsies. Recalibration to the local setting will increase their clinical usefulness and is therefore required before implementation. Unnecessary prostate biopsies while on active surveillance (AS) should be avoided as much as possible. The ability of two calculators to selectively identify men at risk of progression was tested in a large cohort of men with low-risk prostate cancer on AS. The calculators were able to prevent unnecessary biopsies in some men. Usefulness of the calculators can be increased by adjusting them to the characteristics of the population of the clinic in which the calculators will be used.
Publisher: Elsevier BV
Date: 06-2023
Publisher: IOP Publishing
Date: 17-06-2021
Abstract: For decades, dose-volume information for segmented anatomy has provided the essential data for correlating radiotherapy dosimetry with treatment-induced complications. Dose-volume information has formed the basis for modelling those associations via normal tissue complication probability (NTCP) models and for driving treatment planning. Limitations to this approach have been identified. Many studies have emerged demonstrating that the incorporation of information describing the spatial nature of the dose distribution, and potentially its correlation with anatomy, can provide more robust associations with toxicity and seed more general NTCP models. Such approaches are culminating in the application of computationally intensive processes such as machine learning and the application of neural networks. The opportunities these approaches have for in idualising treatment, predicting toxicity and expanding the solution space for radiation therapy are substantial and have clearly widespread and disruptive potential. Impediments to reaching that potential include issues associated with data collection, model generalisation and validation. This review examines the role of spatial models of complication and summarises relevant published studies. Sources of data for these studies, appropriate statistical methodology frameworks for processing spatial dose information and extracting relevant features are described. Spatial complication modelling is consolidated as a pathway to guiding future developments towards effective, complication-free radiotherapy treatment.
No related grants have been discovered for Tiziana Rancati.