Publications by authors named "F Rosica"
Article Synopsis
- The study aimed to validate the effectiveness of autoplanning in whole breast irradiation across various clinical practices and complex cases, involving 24 centers and 240 patients.
- Results showed equivalent target dosimetry between autoplans and manually generated plans, with significant reductions in doses to critical organs, leading to clinician preferences for autoplans in more cases.
- The findings suggest that while autoplanning reduces the manual workload and improves outcomes, adjustments may be needed in some cases to increase clinician satisfaction with the automated plans.
Objectives: This multicentric study was carried out to investigate the impact of small field output factors (OFs) inaccuracies on the calculated dose in volumetric arctherapy (VMAT) radiosurgery brain plans.
Methods: Nine centres, realised the same five VMAT plans with common planning rules and their specific clinical equipment Linac/treatment planning system commissioned with their OFs measured values (OFbaseline). In order to simulate OFs errors, two new OFs sets were generated for each centre by changing only the OFs values of the smallest field sizes (from 3.
Purpose: To investigate the performance of various algorithms for deformable image registration (DIR) for propagating regions of interest (ROIs) using multiple commercial platforms, from computed tomography to cone beam computed tomography (CBCT) and megavoltage computed tomography.
Methods And Materials: Fourteen institutions participated in the study using 5 commercial platforms: RayStation (RaySearch Laboratories, Stockholm, Sweden), MIM (Cleveland, OH), VelocityAI and SmartAdapt (Varian Medical Systems, Palo Alto, CA), and ABAS (Elekta AB, Stockholm, Sweden). Algorithms were tested on synthetic images generated with the ImSimQA (Oncology Systems Limited, Shrewsbury, UK) package by applying 2 specific deformation vector fields (DVF) to real head and neck patient datasets.
Purpose: To investigate the performance of various algorithms for deformable image registration (DIR) to propagate regions of interest (ROIs) using multiple commercial platforms.
Methods And Materials: Thirteen institutions participated in the study with six commercial platforms: RayStation (RaySearch Laboratories, Stockholm, Sweden), MIM (Cleveland, OH, USA), VelocityAI and Smart Adapt (Varian Medical Systems, Palo Alto, CA, USA), Mirada XD (Mirada Medical Ltd, Oxford, UK), and ABAS (Elekta AB, Stockholm, Sweden). The DIR algorithms were tested on synthetic images generated with the ImSimQA package (Oncology Systems Limited, Shrewsbury, UK) by applying two specific Deformation Vector Fields (DVF) to real patient data-sets.
Purpose: To predict patients who would benefit from adaptive radiotherapy (ART) and re-planning intervention based on machine learning from anatomical and dosimetric variations in a retrospective dataset.
Materials And Methods: 90 patients (pts) treated for head-neck cancer (H&N) formed a multicenter data-set. 41 H&N pts (45.