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Record W4360990867 · doi:10.1088/1361-6560/acc7f0

Estimation of patient-size dependent imaging dose for stereoscopic/monoscopic real-time kV image guidance in lung and prostate SBRT

2023· article· en· W4360990867 on OpenAlexafffund
Ruwan Abeywardhana, Alan J. Spurway, Mike Sattarivand

Bibliographic record

VenuePhysics in Medicine and Biology · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsDalhousie UniversityNova Scotia Health Authority
FundersAtlantic Canada Opportunities Agency
KeywordsMedicineNuclear medicineProstateImage-guided radiation therapyLungRadiation treatment planningRadiologyProstate cancerLung volumesMedical imagingRadiation therapyCancer

Abstract

fetched live from OpenAlex

Abstract Purpose. The purpose of this work is to quantify the dependence of patient-specific imaging dose on patient-size from ExacTrac stereoscopic/monoscopic real-time tumor monitoring during lung and prostate stereotactic body radiotherapy (SBRT). Approach . Thirty lung and 30 prostate SBRT patients that were treated with volumetric modulated arc therapy (VMAT) were selected and divided into three patient size categories. Imaging doses from all SBRT fractions were calculated retrospectively assuming patients went through real-time tumor monitoring during their actual VMAT treatment times. Treatment times were divided into periods of stereoscopic and monoscopic real-time imaging depending on the imaging view with linac gantry blockage. The computed tomography (CT) images and contours of the planning target volume (PTV) and organs at risk (OARs) were exported from the treatment planning system. Based on the CT data, patient-specific 3D imaging dose distributions were calculated in a validated Monte Carlo model using DOSEXYZnrc. Vendor-recommended imaging protocols (lung: 120–140 kV, 16–25 mAs; prostate: 110–130 kV, 25 mAs) were used for each patient size category. Patient-specific imaging doses received by PTV and OARs were evaluated using dose volume histograms, dose delivered to 50% of organ volume (D50), and 2% of organ volume (D2). Results . Bone and skin received the highest imaging dose. For the lung patients, the highest D2 for bone and skin were 4.30% and 1.98% of the prescription dose respectively. For prostate patients, the highest D2 were 2.53% and 1.35% of the prescription for bone and skin. Additional imaging dose to PTV as a percentage of the prescribed dose was at most 2.42% for lung and 0.29% for prostate patients. T-test results showed statistically significant difference in D2 and D50 between at least two patient size categories for PTVs and all the OARs. Larger patients received more skin dose in both lung and prostate patients. For the internal OARs, larger patients received more dose in lung treatment while the trend was opposite in prostate treatment. Conclusion . Patient-specific imaging dose was quantified for monoscopic/stereoscopic real-time kV image guidance in lung and prostate patients with respect to patient size. Additional skin dose was 1.98% (in lung patients) and 1.35% (in prostate patients) of the prescription which is within 5% recommended value by the AAPM Task Group 180. For internal OARs, larger patients received more dose in lung patients while the trend was the opposite for prostate patients. Patient size was an important factor to determine additional imaging dose.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0010.000

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 head0.031
GPT teacher head0.375
Teacher spread0.344 · 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 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

Citations3
Published2023
Admission routes2
Has abstractyes

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