MétaCan
Menu
Back to cohort
Record W2517305858 · doi:10.1016/s0167-8140(16)33566-6

167: Measurement of Tumour Hypoxia in Patients with Locally Advanced Non-Small Cell Lung Cancer (NSCLC) Using Positron Emission Tomography (PET) with 18F-Fluoroazomycin Arabinoside (18F-FAZA)

2016· article· en· W2517305858 on OpenAlexaff
Angela Lin, Douglaas Vines, Brandon Driscoll, Lisa W. Le, Stephen Breen, A. Sun

Bibliographic record

VenueRadiotherapy and Oncology · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Hypoxia, and Metabolism
Canadian institutionsBaker Hughes (Canada)University of TorontoUniversity of Calgary
Fundersnot available
KeywordsPositron emission tomographyLung cancerMedicineHypoxia (environmental)non-small cell lung cancer (NSCLC)Nuclear medicineOncologyChemistryOxygen

Abstract

fetched live from OpenAlex

Purpose: Consider a 2x2x1.5 cm basal cell cancer invading right medial canthus periocular embryonic fusion plane.Usual techniques fail: an irregular PTV 4x4x2.5 cm deep, a concave surface, a deep tumour, and adjacent ocular structures.Oculoplastics/Mohs risk enucleation.Electrons with an internal eye shield require bolus and limit energy to 9 MeV.High energy conformal RT risks medial retinal damage.Systemic agents may palliate but do not cure.We describe low energy electron RT (e-) with an orthovoltage (ortho) bump."Bump" modulates energy by replacing some e-dose with ortho to increase surface dose and optimize dose distribution.Bump applies to any anatomic location to a depth of 2-3 cm.Bump can use e-with a tungsten eye shield and ortho for maximal eye-sparing.With orbit invaded, morbidity follows.Radiotherapy may be the best eyepreserving option.Methods and Materials: Central-axis dose calculation using measured % depth dose were compared with central and offcentral axis dose calcs using kVDoseCalc, a dose engine validated in kV cone-beam and ortho therapy; and Monte Carlo for e-offaxis dose calc.We compare conformal RT, arcs, and bump, for periocular cancer cases.We compared central axis data for a 4x4 cm field with: 1) 9 MeV alone; 2) 9 MeV with 0.7 cm custom wax; 3) 9 MeV, 80% of dose, 100 kV DXR bump, SSD 10 cm, 20% of dose; 4) 9 MeV, 80% of dose, 200 kV DXR bump, SSD 50 cm, 20% of dose.Patients treated at our institution in 10 or 20 treatments received 8 or 16 electron treatments (prescribed to account for REB of electrons) and 2 or 4 photons treatments, for a total dose of 45 Gy in 10 fractions, or 50 Gy in 20 fractions.Results: For the case above tables based on measured dose give: Surface dose ( 1) 86%; (2) 90%; (3) 100%; (4) 94% Dmax (100%) (1) 2.0 cm; (2) 1.3 cm; (3) 2.0 cm; (4) 2.0 cm Dose @ 2.7 cm (1) 89%; (2) 58%; (3) 87%; ( 4) 91% Surface and depth refer to skin surface.Dose is normalized: Dmax = 100%.REB and geometry are not included.Comparing dynamic conformal ARCs, VMAT, electrons +/-bolus or tantalum mesh, and bump show the benefits of 9 MeV with 100-200 kV bump.Dose drop off is swift at ~40%/cm beyond D90%.Dose spares eye.Low SSD, low kV bump results in best homogeneity and surface dose; high kV bump gives best dose at depth.Patients can be scanned with a 3D printer wax replica eye shield to reduce artifact and enable accurate dose calculation.Actual patient results are illustrated with isodose distributions; for three clinical cases, the dose above 80% to retina was 2.5 cc for conformal treatment, 1.0 cc for dynamic conformal arc and < 0.5 cc for bumps, demonstrating excellent shielding for the bump technique.Conclusions: Energy modulation with ortho and electrons can result in improved dose distribution.Benefits include: increased treatment depth, improved dose homogeneity, no bolus, increased shield effectiveness, and reduced penumbra; important when treating near the eye.

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)0.000
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 score0.004

Distilled classifier scores by category (both heads)

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 integrity0.0010.000
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 head0.006
GPT teacher head0.233
Teacher spread0.227 · 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

Citations0
Published2016
Admission routes1
Has abstractno

Explore more

Same venueRadiotherapy and OncologySame topicCancer, Hypoxia, and MetabolismFrench-language works237,207