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The impact of multimodality therapies in marginally inoperable soft tissue sarcomas (STS): The Toronto Sarcoma Program (TSP) experience.

2021· article· en· W2507122509 on OpenAlexaffabout
Olga Vornicova, Jay S. Wunder, Peter Chung, Abha A. Gupta, Rebecca A. Gladdy, Charles Catton, Samer Salah, Peter C. Ferguson, Kim M. Tsoi, David Shultz, Savtaj S. Brar, Philip Wong, Carol J. Swallow, Albiruni Ryan Abdul Razak, Esmail Mutahar Al-Ezzi

Bibliographic record

VenueJournal of Clinical Oncology · 2021
Typearticle
Languageen
FieldMedicine
TopicSarcoma Diagnosis and Treatment
Canadian institutionsMount Sinai HospitalPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicineIfosfamideLeiomyosarcomaSoft tissue sarcomaSarcomaInternal medicineProportional hazards modelRadiation therapyOncologySurgeryProgressive diseaseSoft tissueChemotherapyPathologyCisplatin

Abstract

fetched live from OpenAlex

11548 Background: The mainstay therapy of operable STS remains surgery, which may include (neo)adjuvant therapies. Within the TSP, marginally inoperable STS are often treated with sequential chemo (CTX) and radiation (RT) therapy, followed by surgery (SX). Herein we present our experience of multi-modality therapies for marginally inoperable STS patients (pts). Methods: This was a dual-center, single program, retrospective review. Pts were included if deemed to have marginally inoperable primary or recurrent STS, as determined at the TSP tumor board. Pts included must have had CTX with the intent of having RT and SX after. Pts demographics, treatment details and clinical outcomes data were collected. Relapse free survival (RFS) and overall survival (OS) were estimated using the Kaplan-Meier method. Multivariate analysis of the influence of disease characteristics and treatment on outcomes was assessed using Cox regression. Results: From June 2005 to May 2019, 75 pts were identified. Median age was 52 years (range 16-72). Pts were predominantly male (55%). Histological subtypes included dedifferentiated liposarcoma (29%), leiomyosarcoma (27%), synovial sarcoma (19%) and others (25%). Primary tumor was located in the retroperitoneum (48%), extremity (23%), pelvis (12%), thorax (9%), and other sites (8%). All pts had doxorubicin and ifosfamide CTX (median 4 cycles; range 1-6), while RT dose delivered was 50.4Gy/28 fractions in 58 (77%) of cases. Twenty three pts (31%) achieved partial response, 40 pts (53%) had stable disease and 12 pts (16%) had progression of disease (PD) on CTX, of which half (8%) did not undergo further treatment. Nine pts (12%) underwent CTX followed by SX due to significant response, 9 pts (12%) underwent CTX and RT without SX due to persistent tumor unresectability or PD. The final 50 pts (67%) completed multi-modality treatment (CTX, RT & SX). Overall, 59 pts (79%) had SX; negative margins were achieved in 53 (71%). 19 pts (25%) had postoperative complications, causing death in 2 pts (2.7%). With a median follow-up of 72 months, median RFS and OS were 26.9 months (95% CI: 0-86.0), and 65 months (95% CI: 13.5-116.4). Extremity location was associated with superior RFS (median not reached [NR], HR 0.28 95% CI 0.09-0.83, p = 0.022), and OS (median NR, HR 0.29 95% CI 0.09-0.90, p = 0.032). Receipt of RT was associated with superior RFS (median NR, HR 0.23 95% CI 0.10-0.52, p < 0.001); and OS (median NR, HR 0.21 95% CI 0.09-0.50, p < 0.001). Pts who had PD after CTX were associated with poor outcomes - RFS (median 4.7 months, HR 2.03 95% CI 0.61-6.76, p = 0.24); and OS (median 21.9 months, HR 2.48 95% CI 0.73-8.47, P = 0.144). Conclusions: Multi-modality approach resulted in successful resection for most pts with marginally inoperable STS. Extremity location and RT administration were associated with better RFS and OS, while progression on CTX confers worse survival outcomes.

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 score0.966
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.101
GPT teacher head0.507
Teacher spread0.406 · 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".

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Citations0
Published2021
Admission routes2
Has abstractyes

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