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Record W2605314376 · doi:10.1093/neuonc/nox055

Advanced radiation therapeutics for the central nervous system

2017· article· en· W2605314376 on OpenAlexaff
Arjun Sahgal, Paul D. Brown

Bibliographic record

VenueNeuro-Oncology · 2017
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsCentral nervous systemNervous systemNeuroscienceMedicineBiology

Abstract

fetched live from OpenAlex

The field of radiation oncology has changed dramatically over the last decade with respect to technical capabilities. We are now able to treat tumors with extreme precision and are at a paradigm shift of changing the fractionation dogma to shorter courses while still treating with radical intent. In addition, the integration of MRI and particle therapy into mainstream radiation oncology has the potential to further our ability to target brain tumors while sparing critical regions of normal brain tissue. The goal of this supplement is to highlight some of the major changes in radiation therapy as it relates to technology and the central nervous system. More specifically, we provide an up-to-date review of the clinical and technical status as it pertains to the treatment of multiple brain metastases. We have learned from multiple randomized trials that the old standard, whole-brain radiation, is detrimental to a patient’s neuro-cognition and quality of life. Stereotactic radiosurgery is now the dominant treatment for patients presenting with up to 4 metastases. However, current stereotactic radiosurgical technology has overcome the limitations of the past as to the number of lesions treatable. Now, 5, 10, 20 or even more metastases can be focally radiated with excellent local control, both initially and at the time of local or distant brain recurrence. Likewise, larger tumors that were previously under-dosed with single-fraction stereotactic radiosurgery are now treated effectively and safely with high-dose focal hypofractionated stereotactic radiation over the course of a week or so. The rationale and experience with hypofractionated stereotactic radiation is a focus within this supplement. The next paradigm in radiation oncology is emerging because of two dominant technical directions. First, although the use of protons has been a form of therapy for central nervous system tumors for decades, the technology has only recently evolved to allow for modulation of the proton beam. As a result, the technical paradigm of intensity-modulated proton therapy is at the forefront of radiation oncology and will be reviewed in-depth in this supplement specifically as it applies to the central nervous system. Secondly, MRI has been in place for central nervous system tumors for decades as a stand-alone imaging modality that allows for excellent structural information. But the evolution of MRI to allow for biologic information, as a non-invasive method to understand tumor heterogeneity, is only now at the forefront of integration with radiation delivery. With the recent merging of linear accelerator technology with MRI, the ability to image daily and even during radiation delivery brings about a completely new and potentially powerful shift in radiation oncology, especially for the central nervous system. The state-of-the-art of MRI and radiation oncology specific to brain tumors is summarized in this supplement. We hope this supplement informs our readers as to the bright future of radiation oncology for our patients and the commitment to improving outcomes with respect to tumor control while simultaneously reducing adverse effects through advances in the technology itself.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.032
Threshold uncertainty score0.107

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0320.014

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 head0.036
GPT teacher head0.334
Teacher spread0.299 · 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 designNot applicable
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

Citations0
Published2017
Admission routes1
Has abstractyes

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