MétaCan
Menu
← Back to cohort
Record W2070383904 · doi:10.1118/1.4735703

SU-E-T-614: Dose-Reactive Methods in Adaptive Robust Radiation Therapy for Lung Cancer

2012· article· en· W2070383904 on OpenAlexaff
TCY Chan, V.V. Mišić

Bibliographic record

VenueMedical Physics · 2012
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRadiation therapyBreathingMedicineDosimetryNuclear medicineCumulative doseRadiation treatment planningRadiologyAnesthesia

Abstract

fetched live from OpenAlex

PURPOSE: To perform adaptive radiation therapy treatments for lung cancer using IMRT; to show that adjusting the target dose after each fraction, in order to'react' to errors in the dose delivered in prior fractions, can lead to significant changes in the daily tumor dose over the treatment. METHOD AND MATERIALS: Before the start of treatment, the beamlet intensities are optimized to deliver the current target dose distribution for a target set of breathing patterns at minimal healthy tissue dose. In each fraction, the current beamlet intensities are delivered, and the patient's breathing pattern is measured. The breathing pattern set is updated using the breathing pattern, and the target dose distribution is updated to account for dose errors realized in the previous fraction. The beamlet intensities are then re-optimized for the updated dose distribution and uncertainty set, to be used in the next fraction. This process continues until the end of the treatment. We consider three types of updates to the target dose distribution: the reactive± update, which responds to both under and overdose; the reactive- update, which responds only to underdose; and the reactive+ update, which responds only to overdose. RESULTS: On breathing pattern sequences obtained from real patients, dose-reactive methods result in final dose performance comparable to non-dose-reactive methods. However, as the treatment progresses, the reactive± update results in growing daily underdose and overdose, the reactive- update results in growing daily overdose, and the reactive+ update results in growing daily underdose. In contrast, non-reactive methods have stable or decreasing tumor underdose and overdose. CONCLUSIONS: By incorporating dose-reaction, the final tumor dose distribution can be made to conform closely to the target dose distribution, but at the cost of increasing tumor underdose and/or overdose. This increasing heterogeneity may have implications for the biological effectiveness of treatments obtained by dose-reaction.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.033
GPT teacher head0.398
Teacher spread0.365 · 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 designBench or experimental
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
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueMedical Physics→Same topicAdvanced Radiotherapy Techniques→French-language works237,207→