@article {96, title = {Data from: Intensity-modulated proton therapy decreases dose to organs at risk in low-grade glioma patients: results of a multicentric in silico ROCOCO trial}, year = {2018}, publisher = {CancerData}, abstract = {

Background and Purpose

Patients with low-grade glioma (LGG) have a prolonged survival expectancy due to better discriminative tumor classification and multimodal treatment. Consequently, long-term treatment toxicity gains importance. Contemporary radiotherapy techniques such as intensity-modulated radiotherapy (IMRT), volumetric modulated arc therapy (VMAT), Tomotherapy (TOMO) and intensity-modulated proton therapy (IMPT) enable high-dose irradiation of the target but they differ regarding delivered dose to organs at risk (OARs). The aim of this comparative in silico study was to determine these dosimetric differences in delivered doses.

Material and Methods

Imaging datasets of twenty-five LGG patients having undergone postoperative radiotherapy were included. For each of these patients, in silico treatment plans to a total dose of 50.4Gy to the target volume were generated for the four treatment modalities investigated (i.e., IMRT, VMAT, TOMO, IMPT). Resulting treatment plans were analyzed regarding dose to target and surrounding OARs comparing IMRT, TOMO and IMPT to VMAT.

Results

In total 100 treatment plans (4 per patient) were analyzed. Compared to VMAT the IMPT mean dose (Dmean) for 9 out of 10 (90\%) OARs was statistically significantly (p\<0.02) reduced, for TOMO this was true in 3/10 (30\%) patients and for 1/10 (10\%) patients for IMRT. IMPT was the prime modality reducing dose to the OARs followed by TOMO.

Discussion

The low dose volume to the majority of OARs was significantly reduced when using IMPT compared to VMAT. Whether this will lead to a significant reduction in neurocognitive decline and improved quality of life is to be determined in carefully designed future clinical trials.

}, doi = {10.17195/candat.2018.05.1}, author = {Danielle Eekers and Erik Roelofs and Macarena Cubillos-Mesias and Charles Ni{\"e}l and Robert Jan Smeenk and Ann Hoeben and Andre W.H. Minken and Johannes H.A.M. Kaanders and Philippe Lambin and Esther G.C. Troost} } @article {93, title = {The posterior cerebellum, a new organ at risk?}, journal = {Clinical and Translational Radiation Oncology}, volume = {8}, year = {2018}, month = {Jan-01-2018}, pages = {22 - 26}, abstract = {

Eekers et al. have recently proposed a neuro-oncology atlas [doi:10.17195/candat.2017.08.1, Ed.], which was co-authored by most centers associated in the European Proton Therapy Network (EPTN). With the introduction of new treatment techniques, such as integrated magnetic resonance imaging and linear accelerators (MR-linac) or particle therapy, the prediction of clinical efficacy of these more costly treatment modalities becomes more relevant. One of the side-effects of brain irradiation, being cognitive decline, is one of the toxicities most difficult to measure and predict. In order to validly compare different treatment modalities, 1) a uniform nomenclature of the organs at risk (OARs), 2) uniform atlas-based delineation [e.g., Eekers et al.], 3) long-term follow-up data with standardized cognitive tests, 4) a large patient population, and 5) (thus derived) validated normal tissue complication probability (NTCP) models are mandatory.

Apart from the Gondi model, in which the role of the dose to 40\% of both hippocampi (HC) proves to be significantly related to cognition in 18 patients, no similar models are available. So there is a strong need for more NTCP models, on HC, brain tissue and possible other relevant brain structures.

In this review we summarize the available evidence on the role of the posterior cerebellum as a possible new organ at risk for cognition, which is deemed relevant for irradiation of brain and head and neck tumors.

}, keywords = {Cognition, Delineation atlas, Neuro-oncology, Organ at risk, Posterior cerebellum, Radiotherapy}, issn = {24056308}, doi = {10.1016/j.ctro.2017.11.010}, url = {http://linkinghub.elsevier.com/retrieve/pii/S2405630817300769}, author = {Eekers, Dani{\"e}lle B.P. and Lieke in {\textquoteright}t Ven and Deprez, Sabine and Jacobi, Linda and Erik Roelofs and Ann Hoeben and Lambin, Philippe and Dirk De Ruysscher and Troost, Esther G.C.} } @article {65, title = {Data from: Benefit of particle therapy in re-irradiation of head and neck patients. Results of a multicenter in silico ROCOCO trial}, year = {2016}, abstract = {

Background and Purpose

In this multicentric in silico trial we compared photon, proton, and carbon ion radiotherapy plans for re-irradiation of patients with squamous cell carcinoma of the head and neck (HNSCC) regarding dose to tumour and doses to surrounding organs at risk (OARs).

Material and Methods

Twenty-five HNSCC patients with a second new or recurrent cancer after previous irradiation (70 Gy) were included. Intensity-modulated proton therapy (IMPT) and ion therapy (IMIT) re-irradiation plans to, again, a total dose of 70 Gy were compared to photon therapy delivered with volumetric modulated arc therapy (VMAT).

Results

When comparing IMIT and IMPT to VMAT the mean dose to all 22 OARs was statistically significantly reduced for IMIT and to 15 out of 22 OARs (68\%) using IMPT. The maximum dose to 2\% volume (D2) of the brainstem and spinal cord were statistically significantly reduced using IMPT and IMIT compared to VMAT. The data are available on www.cancerdata.org.

Conclusions

In this ROCOCO in silico trial, a reduction in mean dose to OARs was achieved using particle therapy compared to photons in the re-irradiation of HNSCC. There was a dosimetric benefit favouring carbon-ions above proton therapy. These dose reductions may potentially translate into lower severe complication rates related to the re-irradiation.

}, doi = {10.17195/candat.2016.04.2}, author = {Danielle Eekers and Erik Roelofs and Urszula Jelen and Maura Kirk and Marlies Granzier and Filippo Ammazzalorso and Peter H. Ahn and Geert O.R.J Janssens and Frank Hoebers and Tobias Friedmann and Timothy Solberg and Sean Walsh and Esther G C Troost and Johannes H.A.M. Kaanders and Philippe Lambin} } @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} } @article {61, title = {Data from: A validated Tumor Control Probability model based on a meta-analysis of low, intermediate, and high-risk prostate cancer patients treated by photon, proton, or carbon-ion radiotherapy}, year = {2015}, publisher = {CancerData}, abstract = {

This dataset supports the publication by Walsh et al, entitled "A validated Tumor Control Probability model based on a meta-analysis of low, intermediate, and high-risk prostate cancer patients treated by photon, proton, or carbon-ion radiotherapy".

}, doi = {10.17195/candat.2015.10.8}, author = {Sean Walsh and Erik Roelofs and Peter Kuess and Philippe Lambin and Bleddyn Jones and Dietmar Georg and Frank Verhaegen} } @article {57, title = {Data from: Development and Evaluation of an Online Three-Level Proton vs Photon Decision Support Prototype for Head and Neck Cancer - Comparison of Dose, Toxicity and Cost-Effectiveness}, year = {2015}, month = {10/2015}, publisher = {CancerData}, abstract = {

This dataset supports the publication by Cheng et al, entitled "Development and Evaluation of an Online Three-Level Proton vs Photon Decision Support Prototype for Head and Neck Cancer - Comparison of Dose, Toxicity and Cost-Effectiveness". It\ was used to evaluate the PRODECIS decision support prototype\ for online evaluation of eligiblilty for proton therapy using Dutch guidelines.\ 

The Head and Neck test dataset was based on the ROCOCO dataset by HP van der Laan, as described in doi:10.3109/0284186X.2012.692885.

}, keywords = {cancer, decision support, model-based, oncology, proton therapy, radiotherapy planning}, doi = {10.17195/candat.2015.10.5}, url = {https://www.cancerdata.org/resource/doi:10.17195/candat.2015.10.5}, author = {Qing Cheng and Erik Roelofs and Bram Ramaekers and Danielle Eekers and Johan van Soest and Tim Lustberg and Frank Hoebers and Hans Paul van der Laan and Erik Korevaar and Andr{\'e} Dekker and Johannes Langendijk and Philippe Lambin} } @article {33, title = {Benefits of a clinical data warehouse with data mining tools to collect data for a radiotherapy trial}, journal = {Radiotherapy and Oncology}, volume = {108}, year = {2013}, month = {Jan-07-2013}, pages = {174 - 179}, issn = {01678140}, doi = {10.1016/j.radonc.2012.09.019}, author = {Erik Roelofs and Persoon, Lucas and Nijsten, Sebastiaan and Wiessler, Wolfgang and Andr{\'e} Dekker and P. Lambin} } Error | CancerData.org

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