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Record W2056144487 · doi:10.1118/1.4735550

SU‐E‐T‐461: Fractionation Schedule Optimization for Lung Cancer Treatments Using Radiobiological and Dose Distribution Characteristics

2012· article· en· W2056144487 on OpenAlexaff
H. Keller, G. Meier, A. Hope, Matt Davison

Bibliographic record

VenueMedical Physics · 2012
Typearticle
Languageen
FieldMedicine
TopicEffects of Radiation Exposure
Canadian institutionsPrincess Margaret Cancer CentreWestern University
Fundersnot available
KeywordsFractionationRadiation therapyLung cancerNuclear medicineLung volumesDose fractionationMathematicsLungMedicineOncologyRadiologyChemistryInternal medicineChromatography

Abstract

fetched live from OpenAlex

PURPOSE: Lung cancer radiotherapy treatments employ a wide variety of fractionation protocols. The choice among protocols mostly depends on the size of the target volume (GTV or ITV) and the volume of normal tissue receiving a critical dose. Rigorous mathematical criteria for normal tissue (NT) dose distributions were derived to determine the type of dose per fraction schedule that maximizes linear-quadratic tumor effect. METHODS: Selecting the individual doses per fraction that maximize a linear-quadratic effect in the tumor while constraining the normal tissue complication probability according to the Lyman-Kutcher-Burman model leads to an optimization problem. For time-independent parameters, the solution is always an equal dose per fraction schedule; depending on parameter values, two different class solutions are suggested: minimal number of fractions clinically realized with hypo-fractionation, or minimizing dose per fraction clinically realized with standard- or hyper-fractionation. The value of a single scale-free "bifurcation" number, derived from the DVH of the NT dose distribution suggests which solution is preferred for a given plan with respect to a given normal tissue. The clinical relevance of the bifurcation number in selecting fractionation schemes was tested for 30 patients previously treated for non-small-cell lung cancer according to various fractionation protocols. RESULTS: The bifurcation numbers for both lung and esophagus were a good classifier for the hypofractionated and the conventional fractionation groups. The variability of the numbers within patients of the conventional fractionation group was much smaller than the variability of the treated ITV volumes or the ITV to lung volume ratios. The prescribed fractionations were also consisted with the currently accepted alpha-beta values for tumor (10) and radiation-induced pneumonities in the lung (4). CONCLUSIONS: Model-based criteria such as the bifurcation number may replace the more empirical volume criteria to decide the optimal fractionation protocol once the dose distribution has been optimized.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.784
Threshold uncertainty score0.405

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.023
GPT teacher head0.342
Teacher spread0.319 · 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.

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

Citations17
Published2012
Admission routes1
Has abstractyes

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