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Radiological Assessment of the Anatomic Adequacy of Locoregional Lymphatic Radiotherapy Target Volumes for Locally Advanced Breast Cancer (LABC).

2009· article· en· W2067058585 on OpenAlexaff
R. Dinniwell, Grace Lee, Nancy Gregorio, M. Clemons

Bibliographic record

VenueCancer Research · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsMedicineRadiation therapyBreast cancerLymphatic systemDICOMLymphedemaRadiologyLymph nodeNuclear medicineCancerInternal medicinePathology

Abstract

fetched live from OpenAlex

Abstract Background: Women with LABC are at high risk of regional nodal metastases. Failure to control nodal disease can not only result in lymphedema and brachial plexopathy but may also reduce overall survival. Locoregional radiotherapy is typically delivered in an adjuvant manner using standard field borders. An analysis of the anatomic extent of lymphatic nodal disease at the time of presentation of LABC could provide insight into the adequacy of standard locoregional radiotherapy target volumes.Material and Methods: Staging CT and MRI DICOM image sets from a prospective LABC dataset were obtained for analysis. The locations of the lymph node metastases within the lymphatic nodal basins were transposed upon a reference CT DICOM image set in relation to relevant anatomic landmarks. The locoregional lymph nodes were classified into separate categories according to recognized boundaries (Dijkema et al.). Standard four-field borders to treat the breast/chest wall and regional lymphatics were defined and the percent coverage of the lymphatic nodal basins and the lymphatic metastases by 95% of the prescribed dose calculated. Radiotherapy treatment planning was completed using Pinnacle3 software (Version 8.0, Philips Medical System, Bothell, WA, USA).Results: From July 2006 to December 2008, 120 women with LABC were identified. Median age 52 years (range 27 to 93) with 64 left-sided and 56 right-sided primaries (1 cT1N1, 2 cT1N2, 6 cT2N0, 3 cT2N1, 3 cT2N2, 18 cT3N0, 30 cT3N1, 25 cT3N2, 2 cT3N3, 10 cT4N0, 9 cT4N1, 8 cT4N2, 1 cT4N3, 2 not available). The primary disease was situated within: all 4 quadrants and axillary tail in 14, centrally in 10, 3 quadrants in 1, 2 quadrants in 22 and 1 quadrant in 13 of the patients. Of the 60 woman analyzed to date, a total of 165 metastatic lymph nodes were identified by the radiographic staging investigations (127 level I axilla, 20 level II axilla, 4 level III axilla, 2 interpectoral, 2 medial supraclavicular, 1 lateral supraclavicular, 1 infraclavicular, 1 internal mammary, 7 intra-mammary lymph nodes). After placement of the four-field borders all of the lymphatic nodal basins were covered within the treatment fields except for the level I axillary, and lateral and medial supraclavicular regions, where the volume outside of the 95% prescribed dose was 18%, 6% and 28% respectively. With the exception of the level I axillary region, all of the metastatic lymph nodes were encompassed within each of the nodal basins and by the prescribed dose.Conclusions: Increased knowledge of the anatomical distribution of nodal disease in LABC patients prior to systemic therapy facilitates the validation of population based nodal clinical target volumes and affords assurance as to their adequacy. The lymph nodes identified in this series support the current definitions used to account for potential locoregional spread and may be used to further refine specific boundaries. With incorporation of the topographic distribution of gross nodal disease and the improving ability to detect micrometastatic disease, patient specific precision radiotherapy treatment planning is becoming possible. Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 4115.

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)0.004
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 score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
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 head0.029
GPT teacher head0.401
Teacher spread0.373 · 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

Citations1
Published2009
Admission routes1
Has abstractyes

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