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Is it possible to get adequate dose coverage of a clinical target volume (CTV) to account for the possible extra capsular extension in early-stage prostate cancer by low dose rate (LDR) brachytherapy?

2011· article· en· W2560340269 on OpenAlexaff
Elantholi P. Saibishkumar, Douglas Iupati, Jette Borg, K. Fernandes

Bibliographic record

VenueJournal of Clinical Oncology · 2011
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicineBrachytherapyProstate cancerProstateNuclear medicineImplantProstate brachytherapyUnivariate analysisDosimetryRectumStage (stratigraphy)Radiation therapyCancerUrologyRadiologySurgeryMultivariate analysisInternal medicine

Abstract

fetched live from OpenAlex

98 Background: There is no consensus for the definition and evaluation of CTV in post-implant setting for LDR prostate brachytherapy to account for extra capsular extension in clinically localized prostate cancer. In this study, we defined a CTV and evaluated its dosimetry in the post implant CT/MR scans done at 1 month after the LDR brachytherapy procedure. Methods: The initial consecutive 71 patients who underwent LDR brachytherapy under a single physician at Princess Margaret Hospital from June 2009 to July 2010 were included in this retrospective study. On the post implant MRI, the CTV was created by adding 3mm uniform margins around the prostate but respecting the anatomical boundaries like bone, bladder and rectum. Post implant dosimetry was based on CT/MR fusion using the dosimetric parameters V80, V90, V100, V150, V200, D80, D90 and D100. Implants were qualified as optimal if their V100 was >85% and D90 was >90% for both prostate and CTV. Univariate analysis was performed to evaluate associations of factors with V100 and D90 for the CTV using Wilcoxon rank sum test and Fisher's exact test. Results: The mean (SD) prostate V100 and D90 were 95.5% (4.2) and 117% (10) respectively with only 1 patient having sub optimal implant (V100 <85% and D90 <90%). The mean (SD) V100 and D90 for the CTV were also acceptable at 90.6% (4.9) and 103% (9), respectively. Six patients had V100 <85% and 7 patients had D90 <90% for the CTV. On univariate analysis, edema and seed implantation technique correlated with sub-optimal implant for the CTV. The mean (SD) edema for patients with V100 <85% was 18% (10) and with D90 <90% was 15% (12). The corresponding values for the optimal implants both in terms of V100 and D90 were 3% (13). Patients implanted with exclusively loose seeds (15 patients only) had higher incidence of sub-optimal implants (26%) compared to patients who had strands on the antero-lateral margins (56 patients; 3.5%). Conclusions: In this study, adequate dose coverage of CTV was achieved in most patients with current technique but implants with optimal dosimetry to prostate still may have sub-optimal D90 and V100 for the CTV. No significant financial relationships to disclose.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Simulation or modelinglow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Simulation or modelinglow
models agreeAgreement compares identical category sets and study designs across arms.

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)0.005
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 score0.001
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
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 head0.145
GPT teacher head0.458
Teacher spread0.313 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2011
Admission routes1
Has abstractyes

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