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Record W2468817975 · doi:10.1118/1.4958179

TH‐CD‐207A‐10: Using the Gamma Index to Flag Changes in Anatomy During Radiation Therapy of Head and Neck Cancer

2016· article· en· W2468817975 on OpenAlexaff
B Schaly, Jeff Kempe, Sylvia Mitchell, V. Venkatesan, J Battista

Bibliographic record

VenueMedical Physics · 2016
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsWestern University
Fundersnot available
KeywordsHounsfield scaleNuclear medicineRadiation treatment planningWilcoxon signed-rank testPercentileMedicineReceiver operating characteristicCone beam computed tomographyRadiation therapyHistogramPixelImage-guided radiation therapyMathematicsComputed tomographyRadiologyMann–Whitney U testComputer scienceArtificial intelligenceStatistics

Abstract

fetched live from OpenAlex

Purpose: This article presents a fast algorithm for comparing 3‐D anatomy from Cone‐Beam CT (CBCT) imaging using the gamma comparison index and to demonstrate how this can be used to flag patients for possible re‐planning of treatment. Methods: CBCT scans acquired on a Varian linear accelerator during treatment were used as input to the gamma comparator using thresholds of 5 mm distance to agreement and 30 Hounsfield Unit CT number difference. The fraction 1 CBCT study was initially used as the reference. Should there be a re‐plan during treatment, the reference resets to the CBCT study acquired on the day 1 of the re‐plan. Histograms of failing pixels (γ > 1) were generated from each 3‐D gamma map. An indicator of anatomy congruence, the match quality parameter (MQP), was derived from failed pixel histograms using the 90th percentile gamma value. The MQP was plotted versus fraction number and related to actual repeat computed tomography (re‐CT) order dates as decided by a radiation oncologist. From this, decision criteria were derived for the algorithm to “trigger” re‐CT consideration and predictive power was scored using receiver‐operator characteristic (ROC) analysis. Results: The MQP plot generally showed that the on‐line match from CBCT image guidance deteriorated as the treatment progressed due to weight loss and tumor regression. The optimized MQP criteria for triggering re‐CT consideration demonstrated high sensitivity and specificity, consistent with actual re‐CT order dates within ± 3 fractions. Out of 20 patients that were actually re‐planned, the algorithm failed to trigger a re‐CT recommendation only twice and this was caused by CBCT ring artifacts. Conclusion: We have demonstrated that gamma comparisons can be used to evaluate CBCT‐acquired anatomy pairs and, from this, an algorithm can be “trained” to flag patients for possible re‐planning in a manner consistent with local radiation oncology practice.

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)0.002
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.034
GPT teacher head0.345
Teacher spread0.311 · 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 abstractyes

Explore more

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