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 Elisabetta Cagni

Elisabetta Cagni

Research student,


I am a medical physics and I work at the AUSL-IRCCS Hospital ( Reggio Emilia (Italy)), in medical physics and radiotherapy unit since 2009.

I obtained my master degree in Physics at the University of Bologna in 2003. I carried out my master project in the group of Bioelectromagnetism at the University of Bologna. My thesis was titled: "Simplified Model of Ion Channel: a computational approach”.

In 2007 I specialised in medical physics through a 4 years postgraduate course at the University of Bologna. During my post graduate studies, I performed research on the dosimetry of complex radiation therapy treatment with different detectors, at ASMN. My thesis title was "Dosimetric Verification of Intensity Modulated Arc Treatments (IMAT) with an EPID detector”.

My work as medical physics focused on radiotherapy treatment planning and in particular on the implementation and development of automated tools in clinical practice.

Since October 2017 I am a in a in-work PhD student at Cardiff Universiy (Engenireen school). My research project is focused on Automated planning for image-guided radiotherapy.

In January 2022 I delivered my PhD thesis at Cardiff University, titled 'Automated planning for image-guide radiotherapy (AIRPLAN: Automated Image Radiotherapy PLANning) '. 


Research interests

My research in these years has been focused on PhD project reseach.

The title of the PhD project is:

Automated planning for image-guided radiotherapy   

(AIRPLAN: Automated Image-guided Radiotherapy PLANning)

Supervisors: Dr. Emiliano Spezi (ENGIN), Prof Geraint Lewis (ENGIN)

This PhD research project is being performed in collaboration with the Medical Physics Unit and Radiotherapy Department of AUSL-IRCCS, Reggio Emilia (Italy), where i am working as a medical physicist.  One main part of the project is related to a multicentre observational clinical study that involve AUSL-RE Hospital, Erasmus Institute (Rotterdam, The Netherlands) and Cardiff University.

The main points of the research are the following:

  1. Automated treatment planning: Knowledge based tools, Multicriteria optimisation, Pareto solution, inter and intra observer treatment plan evaluation.

  2. Adaptive Radiotherapy: Deformable Image Registration algorithms and Automated re-planning.

Abstract of PhD thesis

Advanced radiotherapy delivery approaches have substantially increased opportunities for sparing organs at risk with proven clinical impact. Ideally, for each iindividual patient, the treatment plan maximally exploits the full potential of the applied delivery technique. Currently, most treatment plans are generated with interactive trial-and-error planning (‘manual planning’). It is well-known that plan quality in manual planning may be sub-optimal, e.g. depending on experience and ambition of the planner, and on allotted planning time. In recent years, several systems for automated plan generation have been developed, often resulting in enhanced plan quality compared to manual planning. Both in manual and automated planning, human evaluation and judgement of treatment plans is crucial. During plan generation, planners usually develop a range of plans, but generally only one or two competing plans are discussed with the radiation oncologist (RO). A necessary assumption for this process to work well, is that (unknown) disparity between planners and ROs on characteristics of good/optimal plans is absent or minor.

Radiotherapy is gradually evolving towards real-time adaptive radiotherapy (ART). ART has the clinical rationale of reducing normal tissue toxicity and improving tumour control through plan adaptation. In this thesis the research in ART was focused on automated methods to standardize ART in predicting the eventual need for re-planning and to assess the goodness of the process. In this thesis the differences between users in perceived quality of plans has been quantified and analysed. Inter-observer differences in plan quality scores were substantial and may result in inconsistencies in generated treatment plans. A method for ART verification, with the ability to quantify registration spatial errors and assess their dose impact at the voxel level, is presented. A systematic workflow to identify effective OAR sparing in re-planning using knowledge-based methods, has been established as a step toward an on-line ART process.

 Aim of the PhD project

The main object of this thesis is the automation of the radiation oncology treatment planning process. In more details, this thesis investigates the implementation and use of automated tools, such as automation in plan generation, plan evaluation and plan adaptation into radiation therapy clinical practice and their impact on treatment quality. Two aspects are focused on, the first being modulated radiation therapy treatment plans (a term including both Intensity Modulated Radiation Therapy, IMRT, and Volumetric Modulated Arc Therapy, VMAT) and the second being to adaptive radiotherapy, including image registration and plan modification.The study was centred mainly on head and neck cancer treatment, however ongoing research using the methods developed during the thesis work, applied to breast cancer treatment is presented.  

Published papers related to PhD project 

  1. Elisabetta Cagni, Andrea Botti, Matteo Orlandi, Marco Galaverni, Cinzia Iotti, Mauro Iori, Geraint Lewis and Emiliano Spezi. "Evaluating the Quality of Patient-Specific Deformable Image Registration in Adaptive Radiotherapy Using a Digitally Enhanced Head and Neck Phantom." Appl. Sci. 2022, 12(19), 9493.

  2. Elisabetta Cagni, Andrea Botti, Agnese Chendi, Mauro Iori, and Emiliano Spezi. "Use of knowledge based DVH predictions to enhance automated re-planning strategies in head and neck adaptive radiotherapy." Physics in Medicine & Biology 66, no. 13 2021: 135004.

  3. Elisabetta Cagni, Andrea Botti, Linda Rossi, Cinzia Iotti, Mauro Iori, Salvatore Cozzi, Marco Galaverni et al. "Variations in head and neck treatment plan quality assessment among radiation oncologists and medical physicists in a single radiotherapy department." Frontiers in oncology 2021: 3714.