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Record W2601095967 · doi:10.1002/mp.12221

Toward a 3D transrectal ultrasound system for verification of needle placement during high‐dose‐rate interstitial gynecologic brachytherapy

2017· article· en· W2601095967 on OpenAlexafffund
Jessica R. Rodgers, Kathleen Surry, Eric Leung, David D’Souza, Aaron Fenster

Bibliographic record

VenueMedical Physics · 2017
Typearticle
Languageen
FieldMedicine
TopicEndometrial and Cervical Cancer Treatments
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreCancer Care OntarioWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBrachytherapyImaging phantomUltrasound3D ultrasoundMagnetic resonance imagingMedicineVaginaDosimetryRadiologyNuclear medicineRadiation therapySurgery

Abstract

fetched live from OpenAlex

PURPOSE: Treatment for gynecologic cancers, such as cervical, recurrent endometrial, and vaginal malignancies, commonly includes external-beam radiation and brachytherapy. In high-dose-rate (HDR) interstitial gynecologic brachytherapy, radiation treatment is delivered via hollow needles that are typically inserted through a template on the perineum with a cylinder placed in the vagina for stability. Despite the need for precise needle placement to minimize complications and provide optimal treatment, there is no standard intra-operative image-guidance for this procedure. While some image-guidance techniques have been proposed, including magnetic resonance (MR) imaging, X-ray computed tomography (CT), and two-dimensional (2D) transrectal ultrasound (TRUS), these techniques have not been widely adopted. In order to provide intra-operative needle visualization and localization during interstitial brachytherapy, we have developed a three-dimensional (3D) TRUS system. This study describes the 3D TRUS system and reports on the system validation and results from a proof-of-concept patient study. METHODS: To obtain a 3D TRUS image, the system rotates a conventional 2D endocavity transducer through 170 degrees in 12 s, reconstructing the 2D frames into a 3D image in real-time. The geometry of the reconstruction was validated using two geometric phantoms to ensure the accuracy of the linear measurements in each of the image coordinate directions and the volumetric accuracy of the system. An agar phantom including vaginal and rectal canals, as well as a model uterus and tumor, was designed and used to test the visualization and localization of the interstitial needles under idealized conditions by comparing the needles' positions between the 3D TRUS scan and a registered MR image. Five patients undergoing HDR interstitial gynecologic brachytherapy were imaged using the 3D TRUS system following the insertion of all needles. This image was manually, rigidly registered to the clinical postinsertion CT scan based on the vaginal cylinder of the needle template. The positions of the tips and the trajectory of the needle paths were compared between the modalities. RESULTS: . In the phantom study, the mean needle tip difference was 1.54 ± 0.71 mm and the mean trajectory difference was 0.94 ± 0.89 degrees (n = 14). In the in vivo study, a total of 73 needles were placed, of which 88% of needles were visible and 79% of tips were identifiable in the 3D TRUS images. Six of the nine needles that were not visible were due to shadowing artifacts created by the presence of the vaginal cylinder of the needle template. The mean distance between corresponding needle tips in the two modalities was 3.82 ± 1.86 mm and the mean trajectory difference was 3.04 ± 1.63 degrees for the five patients. CONCLUSIONS: In this proof-of-concept study, the 3D TRUS system allowed for localization of needles not obscured by shadowing artifacts, providing a method for visualizing needles intra-operatively during HDR interstitial brachytherapy of gynecologic cancers and providing the potential for 3D image-guidance.

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: Empirical
Teacher disagreement score0.594
Threshold uncertainty score0.464

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 head0.037
GPT teacher head0.304
Teacher spread0.266 · 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

Citations29
Published2017
Admission routes2
Has abstractyes

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