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Record W4387133870 · doi:10.1177/17588359231198943

Clinico-demographic characteristics and outcomes of radiation-induced sarcomas (RIS): a CanSaRCC study

2023· article· en· W4387133870 on OpenAlexaffabout
Maurício Fernando Silva Almeida Ribeiro, Hagit Peretz Soroka, Zainab Asif Bhura, Ian Hirsch, Jay S. Wunder, Peter C. Ferguson, Kim M. Tsoi, Savtaj S. Brar, Rebecca A. Gladdy, Carol J. Swallow, Peter Chung, Charles Catton, Philip Wong, Geoffrey Alan Watson, Albiruni Ryan Abdul Razak, Abha A. Gupta, David Shultz

Bibliographic record

VenueTherapeutic Advances in Medical Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicSarcoma Diagnosis and Treatment
Canadian institutionsMount Sinai HospitalPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsMedicineSarcomaProportional hazards modelRadiation therapyInternal medicineSoft tissue sarcomaOsteosarcomaAngiosarcomaOncologyBone SarcomaChemotherapyRetrospective cohort studyMultivariate analysisDatabaseSurgeryPathology

Abstract

fetched live from OpenAlex

Background: Radiation-induced sarcomas (RIS) tend to have aggressive behaviour and because of their rarity, the most appropriate management for these malignancies is uncertain. Objectives: Using the Canadian Sarcoma Research and Clinical Collaboration (CanSaRCC) database, a national sarcoma registry, we aimed to investigate prognostic factors and outcomes for RIS. Design: Retrospective study of RIS patients treated from 1996 to 2021 at three Canadian centres. Methods: RIS was defined as a sarcoma arising in a previously irradiated field following a 3+ year latency period, whose histology was distinct from the initially irradiated tumour. Clinicopathologic and treatment-related information was extracted from the CanSaRCC database. Overall survival (OS) was defined as the time from RIS diagnosis to death from any cause. Response rate (RR) to neoadjuvant chemotherapy (NACT) was based on physician assessment. Time-to-event analyses were estimated using the Kaplan–Meier method, with Cox regression for multivariate analysis. We considered a two-tailed p-value of <0.05 as statistically significant. Results: One hundred seven tumours met the criteria for RIS and were divided into three subgroups: breast angiosarcoma (BAS, n = 54), osteosarcoma (OST, n = 16), and other soft-tissue sarcomas (STS, n = 37). Patients were mostly female ( n = 85, 79%), treated initially for breast carcinomas ( n = 54, 50.5%), and diagnosed with high-grade tumours ( n = 61/71, 86%). None had evidence of synchronous metastasis. Patients with OST were younger (median age: 48 years, p < 0.001), and BAS had the shortest latency interval (8 versus 18 years for OST/STS, p < 0.001). Most patients underwent surgery, 76% ( n = 76/100) R0; 24% ( n = 26) received radiation therapy, mostly ( n = 15, 57.7%) neoadjuvant. Among those receiving chemotherapy, 30 (75%) underwent NACT; among patients with documented response assessment, the RR was 68% ( n = 17/25), being even higher in the BAS population (89.5%, n = 13/17). Median OS was 53 months (95% CI 34–101), with a 5-year OS of 47.6%; larger tumour size, high histologic grade and older age were independent prognostic factors for worse OS. Conclusion: Surgery is standard, and NACT might be useful to downsize large lesions, especially in BAS patients. Raising RIS awareness is fundamental to promoting appropriate management and fostering research through multi-institutional collaborations.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.462
Threshold uncertainty score0.547

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.042
GPT teacher head0.414
Teacher spread0.372 · 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".

Quick stats

Citations7
Published2023
Admission routes2
Has abstractyes

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