MétaCan
Menu
← Back to cohort

Abstract PS15-07: Deep inspiration breath-hold in right-sided breast irradiation: Quantifying the benefit

2021· article· en· W3129484121 on OpenAlexaff
Revathy Krishnamurthy, Grace Lee, Zhihui Liu, Tyler Pittman, Anthony Fyles, Christine Koch

Bibliographic record

VenueCancer Research · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicineInterquartile rangeNuclear medicineBreast cancerRadiation therapyDosimetryRadiation treatment planningLeft breastRadiologyCancerSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Abstract Background: Deep inspiration breath-hold (DIBH) in left-sided breast radiation therapy (RT) is proven to reduce ipsilateral lung and heart doses. The potential benefit of this technique in right-sided breast RT has not been fully explored. We describe the differences in organs at risk (OAR) dosimetry between DIBH and free-breathing (FB) plans in 15 patients who received right-sided breast RT. Materials and methods: Fifteen consecutive patients with right-sided breast cancer who received RT with DIBH between January 1, 2016 and August 31, 2019 were enrolled in this study. All patients initially underwent RT planning with FB scans, and subsequently required DIBH rescanning due to concerns related to exposure of OAR. Dose volume histograms (DVH) for the target volume and OAR coverage were compared between both plans on RayStation Treatment Planning System to quantify the benefit of DIBH. The median value of relative reduction (MRR) and interquartile range in dosimetric parameters were calculated when comparing DIBH to FB. Two-sided Wilcoxon signed-rank test was performed, and a p-value < 0.05 was considered statistically significant. Results: The median age of patients was 64 (38-78). None of the patients had cardiac, respiratory or hepatic comorbidities. The majority of patients (10/15) received locoregional RT (50 Gy in 25 fractions); the remaining 5 patients received breast RT (42.4 Gy in 16 fractions). Tumor bed boost was delivered in 9 of 15 patients. DIBH was delivered throughout RT to 14/15 patients and the clinical goal(s) for which DIBH was introduced was achieved in all cases. DIBH was most commonly used to minimize liver exposure (11/15 patients); in 3 of these 11 patients, reduction in heart or lung exposure was also required. Statistically significant reductions in the imaged liver V5Gy [MRR 89.8% (99 to 71.6, p<0.001)], V10Gy [MRR 94.7% (100 to 77.9, p<0.001)], V20Gy [MRR 97.2% (100 to 84.7, p<0.001)], maximum dose [MRR 15.5% (73.2 to 8, p<0.001)] and average dose [MRR 68.7% (79.4 to 58.6, p<0.001)] were observed with DIBH. Compared to FB, the use of DIBH led to statistically significant reductions in right lung V20 [Median Relative Reduction 20.8% (27.1 to 15.9, p<0.001)], as well as the maximum dose received by the heart and left lung. The target volume coverage was not compromised by DIBH, with at least 99% of the target volume receiving 95% dose in all 15 cases. Conclusion: DIBH for right-sided breast irradiation effectively reduces exposure to liver, lung and heart while maintaining target volume coverage. It can be employed to achieve specific dosimetric goals in the clinical setting. Citation Format: Revathy Krishnamurthy, Grace Lee, Zhihui (Amy) Liu, Tyler Pittman, Anthony Fyles, Christine Anne Koch. Deep inspiration breath-hold in right-sided breast irradiation: Quantifying the benefit [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS15-07.

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)0.001
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
Metaresearch0.0010.001
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)0.0020.000

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 head0.076
GPT teacher head0.382
Teacher spread0.307 · 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
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueCancer Research→Same topicBreast Cancer Treatment Studies→French-language works237,207→