Performance of a Knowledge-Based Model for Optimization of Volumetric Modulated Arc Therapy Plans for Single and Bilateral Breast Irradiation

Antonella Fogliata, Giorgia Nicolini, Celine Bourgier, Alessandro Clivio, Fiorenza De Rose, Pascal Fenoglietto, Francesca Lobefalo, Pietro Mancosu, Stefano Tomatis, Eugenio Vanetti, Marta Scorsetti, Luca Cozzi

Research output: Contribution to journalArticlepeer-review


Purpose To evaluate the performance of a model-based optimisation process for volumetric modulated arc therapy, VMAT, applied to whole breast irradiation. Methods and Materials A set of 150 VMAT dose plans with simultaneous integrated boost were selected to train a model for the prediction of dose-volume constraints. The dosimetric validation was done on different groups of patients from three institutes for single (50 cases) and bilateral breast (20 cases). Results Quantitative improvements were observed between the model-based and the reference plans, particularly for heart dose. Of 460 analysed dose-volume objectives, 13% of the clinical plans failed to meet the constraints while the respective model-based plans succeeded. Only in 5 cases did the reference plans pass while the respective model-based failed the criteria. For the bilateral breast analysis, the model-based plans resulted in superior or equivalent dose distributions to the reference plans in 96% of the cases. Conclusions Plans optimised using a knowledge-based model to determine the dose-volume constraints showed dosimetric improvements when compared to earlier approved clinical plans. The model was applicable to patients from different centres for both single and bilateral breast irradiation. The data suggests that the dose-volume constraint optimisation can be effectively automated with the new engine and could encourage its application to clinical practice.

Original languageEnglish
Article numbere0145137
JournalPLoS One
Issue number12
Publication statusPublished - Dec 1 2015

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Medicine(all)


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