On Quality in Radiotherapy Treatment Plan Optimisation

Hunor Benedek

On Quality in Radiotherapy Treatment Plan Optimisation

Forskningsoutput: Avhandling › Doktorsavhandling (sammanläggning)

257 Nedladdningar (Pure)

Sammanfattning

Målet med strålbehandling är att leverera en tillräckligt hög stråldos för att skada
cellerna i en cancertumör utan att skada omkringliggande friska celler. Olika typer
av celler är olika känsliga för strålning vilket man måste ta hänsyn och idag
görs med datoriserad stråldosplanering. Vid dosplanering provas olika antal strålfält
med olika vinklar och intensitet forma stråldosen att täcka tumören utan att spilla
över för mycket till frisk vävnad. Känsliga organ kan ibland vara väldigt nära
tumören vilket försvårar planeringen och behandling. På senare år har det
introducerats nya tekniker där strålfält roterar runt tumören och fältöppningen
justeras under tiden. Dessa tekniker kallas för intesitetsmodulerad strålbehandling
eftersom strålintensiteten varieras, moduleras, kontinuerligt under behandlingen.
Intensitetsmodulerad behandling möjliggör en skarp gräns mellan ett område med
hög dos och ett med låg dos. Dosplanering av intensitesmodulering kräver en
optimering där en dator söker efter den bästa lösningen för en sorts önskelista som
dosplaneraren väljer. I önskelistan anges hur hög stråldos tumören minst ska få och
samtidigt hur mycket som friska organ maximalt tillåts få.
Problemet är att dosplaneringsystemet sällan ger exakt det man ber om och därför
måste flera gissningar göras för att till slut uppnå önskat resultat. Detta gissande kan
ibland vara svårt och ta lång tid och man vet inte om det verkligen är den bästa
lösningen. Man behöver också säkerställa att den planerade behandlingen är tekniskt
genomförbar. Ett annat problem är att vissa organ rör sig under behandlingen och
kan ”putta” tumören ut ur strålfältet.
I de första två arbetena som ingår i den här avhandlingen har metoder utvecklats för
att systematiskt utvärdera optimeringens kvalité. Metoden användes sedan för att
jämföra plankvalitet från olika optimeraringsmetoder och dosplanerings-system.
I det tredje arbetet har flera tumörrörelsers påverkan av plankavaliten utvärderats
och olika stråltyper har undersökts som en lösning.
I det fjärde och femte arbetet har modeller för artificiell intelligens (AI) utvecklats
för att lära sig hur en strålplan men bra kvalitet ser ut för att sedan skapa bra planer
för nya patienter. I det femte automatiserades metoden från de tidiga arbetena och
användes för inlärningen av AI-modellen.

Originalspråk	engelska
Kvalifikation	Doktor
Handledare	Ceberg, Crister, handledare Knöös, Tommy, Biträdande handledare Bäck, Sven, Biträdande handledare
Tilldelningsdatum	2021 dec. 10
Förlag	Lunds Universitet/Lunds Tekniska Högskola
ISBN (tryckt)	978-91-8039-083-5
Status	Published - 2021

Bibliografisk information

Defence details
Date: 2021-12-10
Time: 13:00
Place: Strålbehandlingshusets föreläsningssal (Torsten Landberg-salen), Plan 3, Klinikgatan 5, Skånes Universitetssjukhus, Lund. Join via zoom: https://www.msf.lu.se/evenemang/disputation-hunor-benedek
External reviewer(s)
Name: Malinen, Eirik
Title: Professor
Affiliation: Department of Medical Physics, Oslo University Hospital, Oslo.
---

Ämnesklassifikation (UKÄ)

Cancer och onkologi
Radiologi och bildbehandling
Annan naturvetenskap

FN:s Globala mål

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Hunor Benedek - WEBB

4 Artikel i vetenskaplig tidskrift

Volumetric modulated arc therapy dose prediction and deliverable treatment plan generation for prostate cancer patients using a densely connected deep learning model
Lempart, M., Benedek, H., Nilsson, M., Eliasson, N., Bäck, S., Munck af Rosenschöld, P., Olsson, L. E. & Jamtheim Gustafsson, C., 2021, I: Physics and imaging in radiation oncology. 19, s. 112-119 8 s.
Forskningsoutput: Tidskriftsbidrag › Artikel i vetenskaplig tidskrift › Peer review

Öppen tillgång
The effect of prostate motion during hypofractionated radiotherapy can be reduced by using flattening filter free beams
Benedek, H., Lerner, M., Nilsson, P., Knoos, T., Gunnlaugsson, A. & Ceberg, C., 2018 apr., I: Physics and imaging in radiation oncology. 6, s. 66-70 5 s.
Forskningsoutput: Tidskriftsbidrag › Artikel i vetenskaplig tidskrift › Peer review

Öppen tillgång
Introducing multiple treatment plan-based comparison to investigate the performance of gantry angle optimisation (GAO) in IMRT for head and neck cancer
Thor, M., Benedek, H., Knöös, T., Engström, P., Behrens, C. F., Hauer, A. K., Sjostrom, D. & Ceberg, C., 2012, I: Acta Oncologica. 51, 6, s. 743-751
Forskningsoutput: Tidskriftsbidrag › Artikel i vetenskaplig tidskrift › Peer review

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title = "On Quality in Radiotherapy Treatment Plan Optimisation",

abstract = "Radiotherapy is one of the essential treatments used in the fight against cancer. The goal of radiotherapy is to deliver a high dose of ionising radiation to the tumour volume and at the same time minimise the effect on healthy tissue by reducing the radiation to critical organs. This contradiction is challenging and has been driving the research and development of the treatments.Over the last two decades, there has been tremendous technical development inradiotherapy. The rapid increase in computational power introduced treatment plan optimisation and intensity-modulated radiotherapy (IMRT). IMRT made it possible to shape the radiation dose distribution closely around the target volume avoiding critical organs to a greater extent. Rotational implementation of IMRT, e.g. Volumetric Modulated Arc Therapy (VMAT) further improved this “dose shaping” ability. With these techniques increasing the ability to produce better treatment plans, there was a need for evaluation tools to compare the treatment plan quality. A plan can be judged by how well it fulfils the prescription and dose-volume constraints, ideally based on treatment outcome. In this work, this is denoted Required Plan Quality, the minimum quality to accept a plan for clinical treatment. If a plan does not fulfil all the dose-volume constraints, there should be a clear priority of which constraints are crucial to achieve. On the other hand, if the constraints are easily fulfilled, there might be a plan of better quality only limited by the treatment systems ability to find and deliver it. This is denoted Attainable Plan Quality in this work– the quality possible to achieve with a given treatment system for a specific patient group.In work described in this thesis, the so-called Pareto front method was used to search for the attainable plan quality to compare different treatment planning systems and optimisation strategies. More specifically, a fall-back planning system for backup planning and an optimiser to find the best possible beam angles. The Pareto method utilises a set of plans to explore the trade-off between target and nearby risk organs.The Pareto plan generation is time-consuming if done manually. The Pareto method was then used in a software that automated the plan generation allowing for a more accurate representation of the trade-off. The software was used to investigate the attainable plan quality for prostate cancer treatments. In the last two publications in this thesis, machine learning approaches were developed to predict a treatment plancloser to the attainable plan quality compared to a manually generated plan.In the thesis, tools have been developed to help move the treatment plan qualityfrom Required Plan Quality towards the Attainable Plan Quality, i.e. the best quality we can achieve with our current system.",

keywords = "Pareto optimisation, Pareto fronts, Dose prediction, Machine learning, Deliverable treatment plans, Volumetric modulated arc therapy, IMRT, patient-specific plan quality, plan quality, Multi Criteria Optimisation, Automated Treatment Planning",

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note = "Defence details Date: 2021-12-10 Time: 13:00 Place: Str{\aa}lbehandlingshusets f{\"o}rel{\"a}sningssal (Torsten Landberg-salen), Plan 3, Klinikgatan 5, Sk{\aa}nes Universitetssjukhus, Lund. Join via zoom: https://www.msf.lu.se/evenemang/disputation-hunor-benedek External reviewer(s) Name: Malinen, Eirik Title: Professor Affiliation: Department of Medical Physics, Oslo University Hospital, Oslo. ---",

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N1 - Defence details Date: 2021-12-10 Time: 13:00 Place: Strålbehandlingshusets föreläsningssal (Torsten Landberg-salen), Plan 3, Klinikgatan 5, Skånes Universitetssjukhus, Lund. Join via zoom: https://www.msf.lu.se/evenemang/disputation-hunor-benedek External reviewer(s) Name: Malinen, Eirik Title: Professor Affiliation: Department of Medical Physics, Oslo University Hospital, Oslo. ---

PY - 2021

Y1 - 2021

N2 - Radiotherapy is one of the essential treatments used in the fight against cancer. The goal of radiotherapy is to deliver a high dose of ionising radiation to the tumour volume and at the same time minimise the effect on healthy tissue by reducing the radiation to critical organs. This contradiction is challenging and has been driving the research and development of the treatments.Over the last two decades, there has been tremendous technical development inradiotherapy. The rapid increase in computational power introduced treatment plan optimisation and intensity-modulated radiotherapy (IMRT). IMRT made it possible to shape the radiation dose distribution closely around the target volume avoiding critical organs to a greater extent. Rotational implementation of IMRT, e.g. Volumetric Modulated Arc Therapy (VMAT) further improved this “dose shaping” ability. With these techniques increasing the ability to produce better treatment plans, there was a need for evaluation tools to compare the treatment plan quality. A plan can be judged by how well it fulfils the prescription and dose-volume constraints, ideally based on treatment outcome. In this work, this is denoted Required Plan Quality, the minimum quality to accept a plan for clinical treatment. If a plan does not fulfil all the dose-volume constraints, there should be a clear priority of which constraints are crucial to achieve. On the other hand, if the constraints are easily fulfilled, there might be a plan of better quality only limited by the treatment systems ability to find and deliver it. This is denoted Attainable Plan Quality in this work– the quality possible to achieve with a given treatment system for a specific patient group.In work described in this thesis, the so-called Pareto front method was used to search for the attainable plan quality to compare different treatment planning systems and optimisation strategies. More specifically, a fall-back planning system for backup planning and an optimiser to find the best possible beam angles. The Pareto method utilises a set of plans to explore the trade-off between target and nearby risk organs.The Pareto plan generation is time-consuming if done manually. The Pareto method was then used in a software that automated the plan generation allowing for a more accurate representation of the trade-off. The software was used to investigate the attainable plan quality for prostate cancer treatments. In the last two publications in this thesis, machine learning approaches were developed to predict a treatment plancloser to the attainable plan quality compared to a manually generated plan.In the thesis, tools have been developed to help move the treatment plan qualityfrom Required Plan Quality towards the Attainable Plan Quality, i.e. the best quality we can achieve with our current system.

AB - Radiotherapy is one of the essential treatments used in the fight against cancer. The goal of radiotherapy is to deliver a high dose of ionising radiation to the tumour volume and at the same time minimise the effect on healthy tissue by reducing the radiation to critical organs. This contradiction is challenging and has been driving the research and development of the treatments.Over the last two decades, there has been tremendous technical development inradiotherapy. The rapid increase in computational power introduced treatment plan optimisation and intensity-modulated radiotherapy (IMRT). IMRT made it possible to shape the radiation dose distribution closely around the target volume avoiding critical organs to a greater extent. Rotational implementation of IMRT, e.g. Volumetric Modulated Arc Therapy (VMAT) further improved this “dose shaping” ability. With these techniques increasing the ability to produce better treatment plans, there was a need for evaluation tools to compare the treatment plan quality. A plan can be judged by how well it fulfils the prescription and dose-volume constraints, ideally based on treatment outcome. In this work, this is denoted Required Plan Quality, the minimum quality to accept a plan for clinical treatment. If a plan does not fulfil all the dose-volume constraints, there should be a clear priority of which constraints are crucial to achieve. On the other hand, if the constraints are easily fulfilled, there might be a plan of better quality only limited by the treatment systems ability to find and deliver it. This is denoted Attainable Plan Quality in this work– the quality possible to achieve with a given treatment system for a specific patient group.In work described in this thesis, the so-called Pareto front method was used to search for the attainable plan quality to compare different treatment planning systems and optimisation strategies. More specifically, a fall-back planning system for backup planning and an optimiser to find the best possible beam angles. The Pareto method utilises a set of plans to explore the trade-off between target and nearby risk organs.The Pareto plan generation is time-consuming if done manually. The Pareto method was then used in a software that automated the plan generation allowing for a more accurate representation of the trade-off. The software was used to investigate the attainable plan quality for prostate cancer treatments. In the last two publications in this thesis, machine learning approaches were developed to predict a treatment plancloser to the attainable plan quality compared to a manually generated plan.In the thesis, tools have been developed to help move the treatment plan qualityfrom Required Plan Quality towards the Attainable Plan Quality, i.e. the best quality we can achieve with our current system.

KW - Pareto optimisation

KW - Pareto fronts

KW - Dose prediction

KW - Machine learning

KW - Deliverable treatment plans

KW - Volumetric modulated arc therapy

KW - IMRT

KW - patient-specific plan quality

KW - plan quality

KW - Multi Criteria Optimisation

KW - Automated Treatment Planning

M3 - Doctoral Thesis (compilation)

SN - 978-91-8039-083-5

PB - Lunds Universitet/Lunds Tekniska Högskola

ER -

On Quality in Radiotherapy Treatment Plan Optimisation

Sammanfattning

Bibliografisk information

Ämnesklassifikation (UKÄ)

FN:s Globala mål

Tillgång till dokument

Fingeravtryck

Forskningsoutput

Volumetric modulated arc therapy dose prediction and deliverable treatment plan generation for prostate cancer patients using a densely connected deep learning model

The effect of prostate motion during hypofractionated radiotherapy can be reduced by using flattening filter free beams

Introducing multiple treatment plan-based comparison to investigate the performance of gantry angle optimisation (GAO) in IMRT for head and neck cancer

Citera det här