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Record W3083705298 · doi:10.3389/fonc.2020.01328

The MOMENTUM Study: An International Registry for the Evidence-Based Introduction of MR-Guided Adaptive Therapy

2020· article· en· W3083705298 on OpenAlexaff
Sophie R. de Mol van Otterloo, John P. Christodouleas, Erwin L. A. Blezer, Hafid Akhiat, Kevin Brown, Ananya Choudhury, Dave Eggert, Bradley A. Erickson, Corinne Faivre‐Finn, Clifton D. Fuller, Joel Goldwein, S. Hafeez, Emma Hall, Kevin J. Harrington, Uulke A. van der Heide, Robert Huddart, Martijn Intven, Anna M. Kirby, Susan Lalondrelle, Claire McCann, Bruce D. Minsky, Stella Mook, Marlies E. Nowee, Uwe Oelfke, Kristina M. Orrling, Arjun Sahgal, Jeffrey G. Sarmiento, Christopher J. Schultz, Robbert J.H.A. Tersteeg, Rob H.N. Tijssen, Alison Tree, Baukelien van Triest, William A. Hall, Helena M. Verkooijen

Bibliographic record

VenueFrontiers in Oncology · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsSunnybrook Health Science Centre
FundersMedical Research CouncilMerck Sharp and DohmeSanofiRosetrees TrustRoyal Marsden NHS Foundation TrustDepartment of Health and Social CareNational Cancer InstituteAccurayKWF KankerbestrijdingNational Institute for Health and Care ResearchCancer Research UKZonMwCilagElektaSiemens HealthineersInnovative Research Group Project of the National Natural Science Foundation of China
KeywordsLinear particle acceleratorMedical physicsMedicineQuality assuranceNuclear medicineBeam (structure)Physics

Abstract

fetched live from OpenAlex

Purpose: MR-guided Radiation Therapy (MRgRT) allows for high-precision radiotherapy under real-time MR visualization. This enables margin reduction and subsequent dose escalation which may lead to higher tumor control and less toxicity. The Unity MR-linac (Elekta AB, Stockholm, Sweden) integrates a linear accelerator with a 1.5T diagnostic quality MRI and an online adaptive workflow. A prospective international registry was established to facilitate the evidence-based implementation of the Unity MR-linac into clinical practice, to systemically evaluate long-term outcomes, and to aid further technical development of MR-linac-based MRgRT. Methods and Results: In February 2019, the Multi-OutcoMe EvaluatioN of radiation Therapy Using the MR-linac study (MOMENTUM) started within the MR-linac Consortium. The MOMENTUM study is an international academic-industrial partnership between several hospitals and industry partner Elekta. All patients treated on the MR-linac are eligible for inclusion in MOMENTUM. For participants we collect clinical patient data (e.g. patient, tumor and treatment characteristics) and technical patient data which is defined as information generated on the MR-linac during treatment. The data are captured, pseudonymized, and stored in an international registry at set time intervals up to two years after treatment. Patients can choose to provide patient-reported outcomes and consent to additional MRI scans acquired on the MR-linac. This registry will serve as a data platform that supports multicenter research investigating the MR-linac. Rules and regulations on data sharing, data access, and intellectual property rights are summarized in an academic-industrial collaboration agreement. Data access rules ensure secure data handling and research integrity for investigators and institutions. Separate data access rules exist for academic and industry partners. This study is registered at ClinicalTrials.gov with ID: NCT04075305 (https://clinicaltrials.gov/ct2/show/NCT04075305). Conclusion: The multi-institutional MOMENTUM study has been set up to collect clinical and technical patient data to advance technical development, and facilitate evidenced-based implementation of MR-linac technology with the ultimate purpose to improve tumor control, survival, and quality of life of patients with cancer.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.077
metaresearch head score (Gemma)0.135
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.077
Threshold uncertainty score0.405

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0770.135
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0110.020
Science and technology studies0.0010.001
Scholarly communication0.0080.006
Open science0.0040.009
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0150.006

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.058
GPT teacher head0.362
Teacher spread0.304 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations151
Published2020
Admission routes1
Has abstractyes

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