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Record W2040845399 · doi:10.1118/1.4814254

SU‐E‐J‐42: Patient Dependent Options for Image Guidance Procedures in Radiotherapy: Prostate Cancer

2013· article· en· W2040845399 on OpenAlexaff
T. Piotrowski, Krzysztof Kaczmarek, Agata Jodda, Adam Ryczkowski, Tomasz Bajon, George Rodrigues, Slav Yartsev

Bibliographic record

VenueMedical Physics · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsTomotherapyProstate cancerMedicineNuclear medicineRadiation treatment planningMargin (machine learning)Image registrationRadiation therapyProstateMedical imagingImage-guided radiation therapyDosimetryFraction (chemistry)CancerMedical physicsRadiologyComputer scienceArtificial intelligenceImage (mathematics)Internal medicine

Abstract

fetched live from OpenAlex

Purpose: Retrospective analysis of image guidance data in radiation treatments of prostate cancer patients allows for simulation of imaging scenarios with less frequent or modified procedures. The correlation of patient specific features with inter‐fraction prostate variations can be explained using patient cohorts with large amounts of daily image data. Methods: The 6,085 setup correction shifts performed during radiotherapy of 216 prostate cancer patients on helical tomotherapy units in two cancer centers were analyzed with respect to automatic and manual matching procedures in co‐registration of planning kV and pre‐treatment MVCT studies. Margins needed to account for inter‐fraction target motion for a daily automatic corrections scheme and three schemes with limited imaging based on one, three and five first fractions as a reference were calculated. The body mass index (BMI) was calculated for all patients and the times required for different steps in the co‐registration process were evaluated. Results: The margin calculated for the daily automatic shift scheme was significantly lower than any of the margins calculated for the limited imaging schemes. The average time needed for management of the daily automatic correction shift during the whole course of the treatment was equal to 5 minutes, and was 30 times shorter than time needed for the daily registration based on automatic and manual correction shifts. Larger setup correction shifts were observed for the patients with higher BMI values. Conclusion: The patients with normal BMI values (BMI<25) require a significantly smaller setup correction and may be treated with a limited number of imaging sessions followed by treatment without imaging using external marks that take into account average systematic shift and personalized margins obtained from the data of the first several fractions. The overweight (25 30) patients may be treated with daily automatic co‐registration and reduced personalized margins.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.292
Teacher spread0.285 · 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 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

Citations0
Published2013
Admission routes1
Has abstractyes

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