@mastersthesis {98, title = {Optimization of Brain and Head \& Neck Radiotherapy}, volume = {Ph.D.}, year = {2018}, month = {10/2018}, pages = {201}, school = {Maastricht University}, address = {Maastricht}, abstract = {

The aim of this thesis is to further optimise radiation therapy of Brain and Head \& Neck by reducing the dose to the healthy surrounding tissue, so called organs at risk (OARs), leading to a reduction in side effects.

The first objective of this doctoral thesis was to assess the value of proton therapy in reducing the dose to the OARs, in particular for re-irradiation in head and neck squamous cell carcinoma and primary irradiation of low-grade glioma. Chapters 2 and 3 report on two in silico trials conducted within the international Radiation Oncology Collaborative Comparison (ROCOCO) consortium, comparing different radiotherapy modalities including proton therapy, to assess the potential gains for individual patients due to the dosimetric characteristics of particle therapy. The first trial compared intensity-modulated proton therapy (IMPT) and carbon-ion therapy (IMIT) with the golden standard volumetric modulated arc therapy (VMAT) when re-irradiating patients with head and neck squamous cell carcinoma. The second trial compared intensity- modulated radiation therapy (IMRT), IMPT and helical tomotherapy (TOMO) with the golden standard VMAT in patients with a low-grade glioma. Both trials demonstrated that particle therapy can significantly reduce the dose to OARs whilst maintaining the prescription dose. Whether this translates into a clinically relevant benefit, is the subject of future research. In order to predict such a benefit, normal tissue complication probability (NTCP) models are needed. Validated NTCP models are currently lacking for head and neck re-irradiation as well as for the primary treatment of the central nervous system (CNS).

In Chapter 4, the posterior cerebellum is introduced as a new, potentially relevant OAR for the future development of an NTCP model that is focused on cognition, based on the growing evidence from structural and functional imaging studies that the cerebellum plays a role in neurocognition.

Delineation of the relevant OARs on computed tomography (CT) and magnetic resonance imaging (MRI) is needed to optimise the treatment plan before administering the corresponding dose to the patient. This manual delineation is a time- consuming process and a well-known source of error within the planning process, due to inter- and intra-observer variability1.

Reducing this CNS OAR delineation variability is the second objective of this thesis, as described in Chapter 5. An international group of expert radiation oncologists in the field of neuro-oncology reached agreement on the European Particle Therapy Network\ (EPTN) consensus-based CNS delineation atlas, in order to decrease the variation in CNS delineation. This CNS atlas is presented online (www.cancerdata.org) and encompasses delineation instructions for 15 CNS OARs, including the posterior cerebellum. It includes one CT-scan at two different brightness and contrast settings and two MR scans (3 and 7 Tesla) showing the OARs in three directions (axial, coronal and sagittal view). In Chapter 6, an EPTN consensus-based normal tissue tolerance table, including all currently known and deemed relevant CNS OARs, reports the tolerance dose in equivalent dose (EQD2), which enables a uniform comparison of different treatment modalities in the future (www.cancerdata.org). The use of the consensus-based EPTN CNS atlas and tolerance table is recommended for the Dutch model-based approach comparing photon and proton beam irradiation as well as for future prospective clinical trials including novel radiation techniques and/or modalities2.

The third objective of this thesis was determining the role of radiotherapy in the treatment of epilepsy. Chapter 7 contains a systematic review about the evidence on the efficacy of primary radiosurgery or stereotactic radiotherapy for drug-resistant non- neoplastic focal epilepsy in adults. After treatment, an average of 58\% of the patients reported no or rare seizures (defined as radiotherapy-adapted Engel class [RAEC] I and II). A dose-effect model was fitted to the available response data to derive a relationship between prescribed dose and RAEC frequency of this, in the Netherlands, new indication for radiotherapy. In Chapter 8, the results of the previous mentioned chapters of this thesis are being discussed and future perspectives are presented. Additional research needs to be conducted to gain further knowledge to fully understand the potential of particle therapy. Thanks to solid collaborations throughout the radiotherapy community and beyond, with all our colleagues in the medical field, there is a unique possibility to further optimise the treatment of Brain and Head \& Neck together.

References

  1. Vinod SK, Jameson MG, Min M, Holloway LC. Uncertainties in volume delineation in radiation oncology: A systematic review and recommendations for future studies. Radiother. Oncol 2016;121:169{\textendash}79.

  2. Widder J, Schaaf van der A, Lambin P, et al. The quest for evidence for proton therapy: the model-based\ approach and precision medicine. Int J Radiat Oncol Biol Phys 2015. pii: S0360-3016:26569-8.\ 

}, keywords = {brain, dissertation, EPTN, H\&N, proton therapy, ROCOCO}, isbn = {978-94-6380-036-5}, doi = {10.17195/candat.2018.12.7}, author = {Danielle B.P. Eekers} } @mastersthesis {70, title = {From Data to Decision - A Knowledge Engineering approach to individualise cancer therapy}, volume = {Ph.D.}, year = {2016}, month = {07/2016}, pages = {175}, school = {Maastricht University}, address = {Maastricht}, abstract = {

Radiation oncology is in trouble. In fact, healthcare in general is. The spending on healthcare is rapidly rising, leading to a non-sustainable situation [1]. An aging population can partly explain the increasing costs, but overtreatment plays a large role as well [2]. This particularly happens due to defensive healthcare [3]. On the other hand, overtreatment also occurs due to the longevity of clinical protocols, which can cause inefficiency, as does the technology-push from healthcare industry.

One of the options to reduce the costs is to apply cost-effectiveness analyses or Healthcare Technology Assessments to inform policy and decision makers about the rightful use of (new) technology.

To this end, the development of personalized decision support systems was investigated, including cost-effectiveness in the field of proton therapy. The following hypotheses were addressed and affirmed:

◆\ \ \ \  Rapid Learning Healthcare (RLHC) can improve clinical research and decision-making.

◆\ \ \ \  Advances in Healthcare Information Technology (HIT) enable international data sharing while preserving patient privacy.

◆\ \ \ \  Combining in silico planning comparison studies with multifactorial prediction models enables cost-effective application of limited, expensive resources, as introduced for proton therapy.

The basic concept of this thesis is the use of RLHC to complement evidence-based medicine with prediction modelling to provide clinical decision support for individualised cancer care (Chapter 2). RLHC applies advanced HIT to aggregate disparate clinical information sources into a data warehouse that presents a complete, integrated disease-oriented view of medical data for research purposes. This chapter presents a general overview of some of the techniques that were used in the following work.

Chapter 3 demonstrates how institutions can benefit from this improved presentation of patient information to improve efficiency and quality of data collection for clinical trials. Furthermore, RLHC uses machine learning principles to mine the data for clinically relevant relations to build data-driven complication prediction models.

Large amounts of data are needed for the modelling and validation of these models, which is unavailable from single institutions. Furthermore, to increase information heterogeneity, which improves the predictive value of models, and to allow for external validation it is imperative that foreign data is available.

Chapter 4 presents the necessary data-sharing techniques that unlock the medical data silos in a privacy-preserving manner by installing {\textquoteleft}connectors{\textquoteright} inside the hospital{\textquoteright}s network firewalls. Furthermore, by transforming the data into internationally standardised semantic interoperable data models, computer systems are able to translate data into knowledge. Furthermore, medical records in local languages are converted into international, unique codes. This means that the data can be reused in foreign countries with equal meaning and value, which allows for federated or distributed learning of complication prediction models [4].

In advance of a federated network, which is very advanced but not common practice yet, the framework of MISTIR (mistir.info) uses a centralised research database for international collaborative in silico clinical trials (Chapter 5). The system offers a secure data-exchange platform with high-quality datasets and trial protocols for multiple in silico planning comparisons of different treatment modalities. Quality assurance measures and automated data extraction procedures are applied for uniform analysis of the results.

Chapter 6 presents a ROCOCO (mistir.info/rococo) lung cancer trial that used the platform to investigate whether proton therapy can reduce dose to normal tissue, with equal or higher tumour dose. The trial participants downloaded de-identified clinical datasets to plan according to a strict protocol. The treatment planning results were returned to the server after which central analysis was performed. It showed that dose reduction is indeed possible, even when escalating the dose to the tumour.

Chapter 7 evaluates an online proton therapy decision support system (PRODECIS: prodecis.nl), reusing head and neck cancer data from another ROCOCO trial. The system assesses healthcare reimbursement eligibility for model-based tumour groups by comparison of photon and proton treatment plans, using publicly available toxicity and cost-prediction models from PredictCancer (predictcancer.org). The system proved successful in the assessment of 92\% of the test cases and showed that, with proton therapy, 91\% of the patients clinically benefit from reduced complications after one year, while 35\% would be considered cost-effective.

1.\ \ \ \ \  CBS. Zorguitgaven stijgen met 1,8 procent in 2014. www.cbs.nl/nl-NL/menu/themas/gezondheid-welzijn/publicaties/artikelen/archief/2015/zorguitgaven-stijgen-met-1-8-procent-in-2014.htm [accessed November 8, 2015]

2.\ \ \ \ \  Patel TY. It Is Overtreatment, Not Overdiagnosis. Acad Radiol 2015;22(8):1044{\textendash}1045. doi:10.1016/j.acra.2015.06.006

3.\ \ \ \ \  Sekhar Ms, Vyas N. Defensive medicine: A bane to healthcare. Ann Med Health Sci Res 2013;3(2):295. doi:10.4103/2141-9248.113688

4.\ \ \ \ \  Damiani A, Vallati M, Gatta R, Dinapoli N, Jochems A, Deist T, et al. Distributed Learning to Protect Privacy in Multi-centric Clinical Studies. In: Holmes JH, Bellazzi R, Sacchi L, Peek N, editors. Artificial Intelligence in Medicine, Springer International Publishing; 2015

}, keywords = {data, decision support, dissertation, PRODECIS, proton therapy, ROCOCO}, isbn = {978 94 6159 577 5}, doi = {10.17195/candat.2016.07.01}, author = {Erik Roelofs} }