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Record W4284894529 · doi:10.3389/fonc.2022.889132

Stereotactic Body Radiotherapy: Hitting Harder, Faster, and Smarter in High-Risk Prostate Cancer

2022· review· en· W4284894529 on OpenAlexaff
Rohann Correa, Andrew Loblaw

Bibliographic record

VenueFrontiers in Oncology · 2022
Typereview
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsHealth Sciences CentreLondon Health Sciences CentreUniversity of TorontoSunnybrook Health Science CentreWestern University
Fundersnot available
KeywordsMedicineProstate cancerContext (archaeology)Radiation therapyProstateMagnetic resonance imagingRadiologyCancerInternal medicine

Abstract

fetched live from OpenAlex

Stereotactic body radiotherapy (SBRT) is a technologically sophisticated form of radiotherapy that holds significant potential to effectively treat high-risk prostate cancer (HRPC). Prostate SBRT has been the subject of intense investigation in the context of low- and intermediate-risk disease, but less so for HRPC. However, emerging data are demonstrating its potential to safely and efficiently delivery curative doses of radiotherapy, both to the prostate and elective lymph nodes. SBRT theoretically hits harder through radiobiological dose escalation facilitated by ultra-hypofractionation (UHRT), faster with only five treatment fractions, and smarter by using targeted, focal dose escalation to maximally ablate the dominant intraprostatic lesion (while maximally protecting normal tissues). To achieve this, advanced imaging modalities like magnetic resonance imaging and prostate specific membrane antigen positron emmission tomography (PSMA-PET) are leveraged in combination with cutting-edge radiotherapy planning and delivery technology. In this focused narrative review, we discuss key evidence and upcoming clinical trials evaluating SBRT for HRPC with a focus on dose escalation, elective nodal irradiation, and focal boost.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.980
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.026
GPT teacher head0.340
Teacher spread0.313 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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

Citations15
Published2022
Admission routes1
Has abstractyes

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