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Record W4404434801 · doi:10.14440/jbm.2024.0047

Evaluation of positioning accuracy in head-and-neck cancer treatment: A cone beam computed tomography assessment of three immobilization devices with volumetric modulated arc therapy.

2024· article· en· W4404434801 on OpenAlexaff
Noor Mail, Khalid M Alshamrani, Rab Nawaz Lodhi, Eman Khawandanh, Amani H. Saleem, Majed Alghamdi, Mohammed Nadershah, Majid Althaqafy, Ahmed Subahi, S. Al-Ghamdi

Bibliographic record

VenuePubMed · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsAmorfix (Canada)
Fundersnot available
KeywordsCone beam computed tomographyHead and neck cancerArc (geometry)Computed tomographyHead and neckMedicineHead (geology)Nuclear medicineBeam (structure)RadiologyBiomedical engineeringRadiation therapyOpticsMathematicsPhysicsSurgeryGeologyGeometry

Abstract

fetched live from OpenAlex

In this study, we assessed the precision and repeatability of the daily patient positioning for three distinct immobilization devices used for head-and-neck patients undergoing RapidArc radiation therapy using cone beam computed tomography (CBCT). An analysis was conducted on the accuracy of patient setup for three distinct immobilization devices, resulting in 1204 CBCT images for 189 patients in total. Using a typical posifix supine headrest and five fixation point podcast-plus-thermoplastic masks, the first group of 39 patients (125 CBCTs) was immobilized. The identical method was used to immobilize the second group of 19 patients (158 CBCTs) in the same posture (supine), and AccuFormTM custom headrests were employed as an added measure. Over 65% of the patients in the third group had a double shell positioning system (DSPS) covering their entire head and neck. Patient-alignment-accuracy or couch shifts in the vertical, longitudinal, and lateral directions from CT-CBCT fusions were recorded from ARIA. Our results showed that in 90% of the anteriorposterior (AP), 90% of the superior-inferior (SI), and 92.7% of the right-left (RL) population in the first group, patient-alignment-accuracy or couch shifts were within 2 mm. For 99.4% (AP), 100% (SI), and 98.7% (RL) of the second group's total population, patient-alignment-accuracy was within 2 mm. In the third group, it was within 2 mm for 92.1% (AP), ~89% (SI), and 93.3% (RL) of the total population. In conclusion, a significant improvement was seen with the application of a mouth bite and a tailored backrest cushion to the five fixation point posicast mask. In addition, significant improvement in the alignment of the lower neck area was observed with the use of DSPS. Virtually 100% of the head-and-neck patients were aligned within an accuracy of 3 mm, which is the PTV margin in our department.

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)0.005
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 score0.002
Threshold uncertainty score0.006

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 studies0.0000.000
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 head0.030
GPT teacher head0.322
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
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

Citations2
Published2024
Admission routes1
Has abstractyes

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