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Record W2102507906 · doi:10.1088/0031-9155/55/4/022

IMRT for the breast: a comparison of tangential planning techniques

2010· article· en· W2102507906 on OpenAlexaff
Wendy Smith, Geetha Menon, Nathan Wolfe, Nicolas Ploquin, Theresa Trotter, Delia Pudney

Bibliographic record

VenuePhysics in Medicine and Biology · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsOttawa HospitalAlberta Cancer FoundationUniversity of AlbertaUniversity of Calgary
Fundersnot available
KeywordsRadiation treatment planningCollimatorNuclear medicineMedicineRadiation therapyMedical physicsComputer scienceBreast cancerRadiologyCancerOpticsPhysics

Abstract

fetched live from OpenAlex

Three intensity-modulated tangential beam radiotherapy plan types for breast cancer treatment were evaluated based on PTV homogeneity index (HI) and equivalent uniform dose (EUD), heart V30 and EUD, whole lung V20 and EUD, and typical planning time compared to conventional 2D plans. 20 early-stage breast cancer patients were CT-scanned in the supine position, and tangential field extent, gantry and collimator angles were chosen. Four treatment plans were created for each patient: conventional, dynamically wedged plan based on the dose distribution on the central axial slice; forward planned IMRT; surface compensated plan created using an Eclipse tool and hybrid IMRT plan combining open and inverse-optimized fields. All three IMRT planning techniques represent significant improvement in PTV HI and EUD compared to conventional plans. Among the IMRT plans, the hybrid IMRT plan produced the best HI. IMRT lowered heart V30 and lung V20, but no significant differences in heart or lung EUD were detected between IMRT techniques. The IMRT technique with the shortest planning time was the compensated plan, followed by the hybrid IMRT. IMRT planning provides dosimetric benefits in breast cancer patients. The selection of the most appropriate IMRT technique must include careful consideration of the resources available.

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 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 score0.870
Threshold uncertainty score0.186

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.117
GPT teacher head0.454
Teacher spread0.337 · 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 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

Citations53
Published2010
Admission routes1
Has abstractyes

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