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Record W3041869968 · doi:10.1017/s1460396920000503

Dosimetric evaluation of 3 and/or 4 field radiation therapy of breast cancers: clinical experience

2020· article· en· W3041869968 on OpenAlexaffabout
Ernest Osei, Susan Dang, Johnson Darko, Katrina Fleming, Ramana Rachakonda

Bibliographic record

VenueJournal of Radiotherapy in Practice · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsUniversity of GuelphGrand River HospitalUniversity of Waterloo
Fundersnot available
KeywordsMedicineLumpectomyBreast cancerRadiation therapyMastectomyCancerRadiologyAdjuvant radiotherapyOncologyInternal medicine

Abstract

fetched live from OpenAlex

Abstract Background: Breast cancer is the most commonly diagnosed cancer among women and the second leading cause of cancer-related death in Canadian women. Surgery is often the first line of treatment for low-risk early stage patients, followed by adjuvant radiation therapy to reduce the risk of local recurrence and prevent metastasis after lumpectomy or mastectomy. For high-risk patients with node positive disease or are at greater risk of nodal metastasis, radiation therapy will involve treatment of the intact breast or chest-wall as well as the regional lymph nodes. Materials and methods: We retrospectively evaluated the treatment plans of 354 patients with breast cancer with nodes positive or were at high risk of nodal involvement treated at our cancer centre. All patients were treated with a prescription dose of 50 Gy in 25 fractions to the intact breast or chest-wall and 50 Gy in 25 fractions to the supraclavicular region and, based on patient suitability and tolerance, were treated either using the deep inspiration breath hold (DIBH) or free-breathing (FB) techniques. Results: Based on patient suitability and tolerance, 130 (36·7%) patients were treated with DIBH and 224 (63·3%) with FB techniques. There were 169 (47·7%) patients treated with intact breast, whereas 185 (52·3%) were treated for post-mastectomy chest-wall. The mean PTV_eval V 92% , V 95% , V 100% and V 105% for all patients are 99·4 ± 0·7, 97·6 ± 1·6, 74·8 ± 7·9 and 1·5 ± 3·2%, respectively. The mean ipsilateral lung V 10Gy , V 20Gy and V 30Gy are 30·0 ± 5·3, 22·4 ± 4·7 and 18·4 ± 4·3% for intact breast and 30·9 ± 5·8, 23·5 ± 5·4 and 19·4 ± 5·0% for post-mastectomy patients with FB, respectively. The corresponding values for patients treated using DIBH are 26·3 ± 5·9, 18·9 ± 5·0 and 15·6 ± 4·7% for intact breast and 27·5 ± 6·5, 20·6 ± 5·7 and 17·1 ± 5·2% for post-mastectomy patients, respectively. The mean heart V 10Gy , V 20Gy , is 1·8 ± 1·7, 0·9 ± 1·0 for intact breast and 3·1 ± 2·2, 1·7 ± 1·6 for post-mastectomy patients with FB, respectively. The corresponding values with the DIBH are 0·5 ± 0·7, 0·1 ± 0·4 for intact breast and 1·1 ± 1·4, 0·4 ± 0·7 for post-mastectomy patients, respectively. Conclusion: The use of 3 and/or 4 field hybrid intensity-modulated radiation therapy technique for radiation therapy of high-risk node positive breast cancer patients provides an efficient and reliable method for achieving superior dose uniformity, conformity and homogeneity in the breast or post-mastectomy chest-wall volume with minimal doses to the organs at risk. The development and implementation of a consistent treatment plan acceptability criteria in radiotherapy programmes would establish an evaluation process to define a consistent, standardised and transparent treatment path for all patients that would reduce significant variations in the acceptability of treatment plans.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.721
Threshold uncertainty score0.241

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.061
GPT teacher head0.423
Teacher spread0.362 · 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 teacher head, 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".

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Citations4
Published2020
Admission routes2
Has abstractyes

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