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Record W2979036703

Reducing errors in prostate tracking with an improved fiducial implantation protocol for CyberKnife based stereotactic body radiotherapy (SBRT).

2018· article· en· W2979036703 on OpenAlexaff
Oliver Holmes, Julie Gratton, J. Szántó, Eric Vandervoort, Janice Doody, Elizabeth Henderson, Scott C. Morgan, Joseph P. O’Sullivan, Shawn Malone

Bibliographic record

VenuePubMed · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsDr. H. Bliss Murphy Cancer CentreUniversity of OttawaOttawa HospitalMemorial University of Newfoundland
Fundersnot available
KeywordsFiducial markerCyberknifeMedicineProstate cancerProstateRadiologyRadiation therapyNuclear medicineRadiosurgeryCancerInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

PURPOSE: Ultra-hypofractionated radiotherapy with SBRT is an established technique for treating localized prostate cancer. CyberKnife based SBRT requires implantation of fiducial markers for soft tissue target tracking by the orthogonal KV X-ray imaging system. The spatial distribution of fiducial markers must allow accurate calculation of a 3D transformation that describes the position of the prostate within the reference frame of the planning CT scan. Accuray provides a fiducial implantation guideline for tracking soft tissue lesions. Despite using the guideline we experienced an unacceptably high rate of rotational tracking failure due to problems with fiducial placement. We adapted the Accuray guideline to prostate SBRT for improved fiducial placement and more reliable target tracking.Methods and materials: 54 patients with prostate adenocarcinoma were treated with ultra-hypofractionated radiotherapy on CyberKnife. Patients had platinum fiducial markers implanted transrectally under ultrasound guidance by a Radiologist. For the first 26 patients, fiducial markers were positioned following the Accuray fiducial placement guidelines for soft tissue lesions (cohort 1). The initial rotational tracking error rate was unacceptably high (23%). On review, inappropriate fiducial placement was identified as the cause of error (especially insufficient spacing between seeds). In October 2016 we developed a seed placement protocol specifically for implanting fiducial markers within the prostate and a second cohort of patients was treated thereafter (cohort 2, 28 patients). The stipulations of the original guideline are maintained while the modified protocol requires that 4 fiducial markers be implanted in the postero-lateral peripheral zone in a single coronal plane. RESULTS: In cohort 1, patients had a median age of 64 years (50 - 74), PSA of 6.6mcg/L (1.1 - 14.7), and prostate volume of 56 cc (22 - 125), while in cohort 2 they had a mean age of 65 years (53 - 75), PSA of 6.2 mcg/L (1 - 12) and prostate volume of 47 cc (21 - 106). The fiducial markers were easily visualized and there were no cases of urosepsis related to fiducial implantation. In 6 of 26 patients (23%) from cohort 1, only translational mapping without accurate spatial rotations could be calculated. After adopting the prostate specific fiducial implantation protocol, rotational tracking error was eliminated. Accurate 6 degree tracking (accounting for translations and rotations) was achieved in all 28 patients from cohort 2. Using an in-house computer script we analyzed the dose distributions resulting from rotational misalignments of -10, -5, -3, 3, 5, and 10 degrees along all three rotational axes (pitch, roll and yaw). Rotational misalignments result in decreased minimum dose to the PTV and increased maximum dose to OARs. CONCLUSION: Implementing a prostate specific fiducial placement protocol for SBRT significantly improved our ability to track prostate motion in 6 degrees 77% to 100% reliability. Failure to track rotations can potentially lead to underdosing and overdosing of portions of the prostate and OARs respectively.

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)0.004
metaresearch head score (Gemma)0.007
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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
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 science0.0010.001
Research integrity0.0010.001
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 head0.020
GPT teacher head0.313
Teacher spread0.293 · 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
GenreMethods

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

Citations18
Published2018
Admission routes1
Has abstractyes

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