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Record W2114374644 · doi:10.1017/s1460396914000193

Image guidance procedures in radiotherapy for prostate cancer and the influence of body mass index

2014· article· en· W2114374644 on OpenAlexaff
T. Piotrowski, Krzysztof Kaczmarek, Agata Jodda, Adam Ryczkowski, Tomasz Bajon, George Rodrigues, Slav Yartsev

Bibliographic record

VenueJournal of Radiotherapy in Practice · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsTomotherapyOverweightProstate cancerMedicineBody mass indexNuclear medicineProstateCancerRadiologyRadiation therapyInternal medicine

Abstract

fetched live from OpenAlex

Abstract Purpose To investigate possible optimisation of the image guidance procedure for the prostate cancer patients with respect to imaging frequency and patient body mass index (BMI). Methods The 6,085 setup correction shifts and BMI for 216 prostate cancer patients treated on tomotherapy units in two centres were analysed. Margins needed to account for inter-fraction target motion with daily only automatic correction and with automatic and manual corrections during one, three or five first fractions as a reference for further treatment without imaging were calculated. Results The planning target volume margin calculated for the daily automatic correction only scheme was significantly lower than the margins calculated for the image guidance limited to a few initial fractions. Manual corrections after automatic fusion were more important for patients with higher BMI. On average, the patients with normal BMI had manual correction shift of 0·7 mm in anterioposterior direction, while overweight and obese patients required, correspondingly, the shifts of 1·3 and 1·4 mm. Conclusion Overweight and obese patients require daily imaging with time saving available by performing automatic kV/MV computed tomography registration only. The patients with normal BMI may be treated with imaging guidance during a few initial treatment fractions.

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)0.002
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.417
Threshold uncertainty score0.622

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.004
GPT teacher head0.313
Teacher spread0.309 · 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

Citations7
Published2014
Admission routes1
Has abstractyes

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