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Abstract P3-19-23: Radiation-induced sarcomas of the breast: A 20-year single-centre experience

2022· article· en· W4220802109 on OpenAlexaffabout
Vanessa Di Lalla, Marwan Tolba, Farzin Khosrow‐Khavar, Ayesha Baig, Carolyn Freeman, Valérie Panet-Raymond

Bibliographic record

VenueCancer Research · 2022
Typearticle
Languageen
FieldMedicine
TopicVascular Tumors and Angiosarcomas
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsMedicineSarcomaBreast cancerLeiomyosarcomaIncidence (geometry)Cancer registryRadiation therapyCancerRadiologyInternal medicinePathology

Abstract

fetched live from OpenAlex

Abstract Background: Radiation-induced sarcomas (RIS) are rare malignancies comprising only 2.5-5.5% of all sarcomas. As per the Cahan criteria, diagnosis requires histologically proven sarcoma within or around a previously irradiated site. Breast cancer patients are known to have a higher incidence of RIS compared to other primary solid cancers. Following breast irradiation, RIS typically occur after a prolonged latency up to 20 years, although this is often shorter for angiosarcomas. A clear mechanism for RIS development has not yet been elucidated, and prognosis remains poor given limited treatment options. This study aimed to review incidence, risk factors, management, and subsequent oncologic outcomes of breast RIS using 20-year experience at the McGill University Health Centre (MUHC), a large tertiary care centre. Methods: Using our institutional cancer registry database, we identified patients with histologically proven sarcomas of the breast diagnosed between the years 2000 to 2020. We then identified and included patients meeting the Cahan criteria as described above. Patient data including demographics, oncologic treatment of primary breast cancer as well as subsequent sarcoma, and oncologic outcomes were collected from our electronic medical record systems. Descriptive statistics were used to describe patient demographic data. Oncologic outcomes were assessed using the Kaplan Meier method. Results: From 2000 to 2020, we identified 19 patients with breast RIS: 11 angiosarcomas (57.9%), 3 osteosarcomas (15.8%), 2 carcinosarcomas (10.5%), 2 undifferentiated pleomorphic sarcomas (10.5%) and 1 high-grade leiomyosarcoma (5.3%). The median age at RIS diagnosis was 72 years (range 39-82, mean 67) and median latency period for development of RIS was 112 months (range 53-300, mean 120). In terms of secondary treatment, all patients underwent surgery, either total or partial mastectomy (n=14 and n=5, respectively), 3 patients received systemic therapy, and 6 patients received re-irradiation. The median follow-up time was 31 months (range 6-172, mean 48) from diagnosis of RIS. Overall, 5 patients had recurrence at the site of sarcoma and 1 patient developed distant metastases through the study follow-up. The median time to progression was 7 months (range 4-14). The progression-free survival (95% CI) at two years was 56.1 % (37.4-84.4%). At two years follow-up after sarcoma diagnosis, 2 patients were deceased, resulting in an overall survival (95% CI) of 88.9% (75.5-100%). Conclusion: Our 20-year institutional experience confirms that while RIS of the breast remains rare, when managed in a high patient-volume centre, overall survival outcomes appear favorable when salvage is possible. However, a significant proportion of patients still recur locally after maximal treatment, confirming the aggressive nature of this disease and suggesting further follow-up is needed for survival data maturation. Given the rarity of this disease in the context of limited treatment options, patients with RIS should be ideally managed in high-volume centres where multidisciplinary management is available. Citation Format: Vanessa Di Lalla, Marwan Tolba, Farzin Khosrow-Khavar, Ayesha Baig, Carolyn Freeman, Valerie Panet-Raymond. Radiation-induced sarcomas of the breast: A 20-year single-centre experience [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P3-19-23.

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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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.594
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.074
GPT teacher head0.363
Teacher spread0.289 · 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.

Study designBench or experimental
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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Citations0
Published2022
Admission routes2
Has abstractyes

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