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Record W2807789927 · doi:10.1016/j.jmir.2019.05.005

Stability of Intracavitary Applicator Placement for HDR Brachytherapy of Cervix Cancer

2019· article· en· W2807789927 on OpenAlexaff
Alexandra Balsdon, Emilia Timotin, Robert Hunter, Kevin R. Diamond

Bibliographic record

VenueJournal of medical imaging and radiation sciences · 2019
Typearticle
Languageen
FieldMedicine
TopicEndometrial and Cervical Cancer Treatments
Canadian institutionsJuravinski Cancer CentreMcMaster UniversityMcMaster University Medical Centre
Fundersnot available
KeywordsMedicineRadiographyBrachytherapyNuclear medicineFluoroscopyDosimetryCervical cancerRadiation therapyRadiologyCancer

Abstract

fetched live from OpenAlex

INTRODUCTION/BACKGROUND: Cervical cancer is often treated with a combination of external beam radiation therapy and high-dose-rate intracavitary brachytherapy. An intrauterine ring and tandem applicator is used for intracavitary brachytherapy. The dose is prescribed to the high-risk clinical target volume. The goals of this study were to investigate the stability of intracavitary applicator placement during patient transfer and to evaluate the dosimetric impact of displacement. METHODS: Fourteen patients with cervical cancer were analyzed. Three sets of orthogonal fluoroscopic radiographs were obtained in the high-dose-rate suite after the insertion and before treatment: pre-computed tomography (CT) fluoroscopic radiograph with patient in the lithotomy position, pre-CT fluoroscopic radiograph with patient in the legs down position, and post-CT fluoroscopic radiograph with patient in the legs down position. Applicator position after CT was compared with the pre-CT radiographs to determine if the position changed during patient transfer. The displacement was measured in the anterior-posterior, medio-lateral, and superior-inferior directions, as well as the degree of pitch, roll, and yaw. To study the impact of applicator shifts on dose to organs at risk (OARs), the ring and tandem applicator was shifted virtually in the BrachyVision treatment planning system. The OARs studied included the small bowel, sigmoid colon, rectum, and bladder. Five millimeter shifts were made in the superior-inferior, medio-lateral, and anterior-posterior direction. Three degree rotations were made in the pitch, yaw, and roll directions. Applicator shifts were analyzed in only one direction at a time. The dosimetric impact on OARs was evaluated by comparing the original and shifted/rotated plans to dose-volume histogram-based criteria. RESULTS: The average displacements were 1.9 ± 0.5 mm laterally, 3.0 ± 0.6 mm longitudinally, and 9.5 ± 1.5 mm anterior-posterior. The average applicator rotation on the posterior-anterior radiograph was 1.0 ± 0.2° and 2.6 ± 0.6° on the lateral radiograph. Five millimeter anterior-posterior shifts had the greatest effect on dose to OARs. On average, 5 mm anterior shifts had the greatest effect on the small bowel dose, where there was a 13.7% (79.6 cGy) increase in D2cc. Five millimeter anterior shifts also affected bladder dose, with a 36.5% (141.1 cGy) increase in D2cc. Five millimeter POST shifts increased the rectal D2cc by 28.6% (168.7 cGy). Other directional shifts had negligible effects on dose. The largest effect on OAR dose arising from rotations was to the sigmoid colon, when the applicator rotated in the POST pitch direction. As a result, the dose increased by 4.7% (7.6 cGy). All other rotations had minimal impact on OAR doses. CONCLUSION: Patient transfer resulted in applicator shifts and rotations that had a measurable effect on dose to OARs. The displacements were the result of either a direct shift or rotation of the applicator. Additional tracking of these shifts and rotations may clarify the sources of these unwanted motions and suggest possible mitigation strategies.

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)0.002
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
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.026
GPT teacher head0.369
Teacher spread0.343 · 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".

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Citations6
Published2019
Admission routes1
Has abstractyes

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