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Record W4313544673 · doi:10.1200/jco.22.01096

Risk Factors for Local Failure Following Chemoradiation and Magnetic Resonance Image–Guided Brachytherapy in Locally Advanced Cervical Cancer: Results From the EMBRACE-I Study

2023· article· en· W4313544673 on OpenAlexaff
Maximilian Schmid, Jacob Christian Lindegaard, Umesh Mahantshetty, Kari Tanderup, Ina M. Jürgenliemk‐Schulz, Christine Haie-Méder, Lars Fokdal, Alina Sturdza, Peter Hoskin, Barbara Šegedin, Kjersti Bruheim, Fleur Huang, Bhavana Rai, Rachel Cooper, Elzbieta van der Steen-Banasik, Erik Van Limbergen, Bradley R. Pieters, Primož Petrič, Dariga Ramazanova, Robin Ristl, Sadhana Kannan, Rohini Hawaldar, Stefan Ecker, Kathrin Kirchheiner, Li Tee Tan, Remi A. Nout, N. Nesvacil, Astrid de Leeuw, Richard Pötter, Christian Kirisits, Melissa Christiaens, Hilde Janssen, Marit Sundset, Ludy Lutgens, E. Villafranca, Janaki Hadjiev, François Bachand, Bradley A. Erickson, G. Jacobson, Maarit Anttila, Isabelle Dumas, Gerry Lowe, Robert Hudej, Taran Paulsen Hellebust, Geetha Menon, Arun S. Oinam, Peter Bownes, Marisol De Brabandere, B.J. Oosterveld, Kees Koedooder, Anne Beate Langeland Marthinsen, Diane Whitney, Martijn Ketelaars, Brigitte Reiniers, Itxa Mora, Gergely Antal, Deidre Batchelar, Jason Rownd, Yusung Kim, Jan-Erik Palmgren, R. Mazeron, Cyrus Chargari, Sofia Spampinato

Bibliographic record

VenueJournal of Clinical Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicEndometrial and Cervical Cancer Treatments
Canadian institutionsUniversity of Alberta
FundersUniversiteit van AmsterdamAmerican Society of Clinical OncologyPfizer
KeywordsMedicineBrachytherapyMagnetic resonance imagingCervical cancerRadiation therapyProportional hazards modelExternal beam radiotherapyRadiologyNuclear medicineInternal medicineCancer

Abstract

fetched live from OpenAlex

PURPOSE To report clinical and treatment characteristics, remission and failure patterns, and risk factors for local failure (LF) from the EMBRACE-I study. MATERIALS AND METHODS EMBRACE-I was a prospective, observational, multicenter cohort study on magnetic resonance imaging–based image-guided adaptive brachytherapy (MR-IGABT) in locally advanced cervical cancer. Treatment consisted of external beam radiotherapy, concurrent chemotherapy, and MR-IGABT. LF was defined as progressive or recurrent disease in the cervix, uterus, parametria, pelvic wall, or vagina. Competing risk analysis was used to estimate local tumor control (LC) and Cox proportional regression models for multivariable analysis and dose-response analysis. RESULTS One thousand three hundred eighteen patients with a median follow-up of 52 months were available for this analysis. Eighty-one patients had persistent disease 3 months after end of treatment. Of those, 60 patients achieved LC at 6-9 months without further treatment, whereas 21 patients had progressive disease. In addition, 77 patients developed a local recurrence after complete remission comprising a total number of 98 LFs. LFs were located inside the MR-IGABT target volumes in 90% of patients with LF. In multivariable analysis, histology, minimal dose to 90% of high-risk clinical target volume (CTVHR), maximum tumor dimension, CTVHR > 45 cm3, overall treatment time, tumor necrosis on magnetic resonance imaging at diagnosis, uterine corpus infiltration at diagnosis and at MR-IGABT, and mesorectal infiltration at MR-IGABT had significant impact on LF. Dose-response analysis showed that a minimal dose to 90% of 85 Gy to the CTVHR led to 95% (95% CI, 94 to 97) LC 3 years postintervention for squamous cell in comparison to 86% (95% CI, 81 to 90) for adeno/adenosquamous carcinoma histology. CONCLUSION The present study demonstrates the safety and validity of the GYN GEC-ESTRO/ICRU-89 target concept and provides large-scale evidence for dose prescription and new risk factors for LF in MR-IGABT in locally advanced cervical cancer.

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)0.002
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.076
GPT teacher head0.441
Teacher spread0.365 · 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

Citations80
Published2023
Admission routes1
Has abstractyes

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