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Abstract OT1-07-01: MRI screening versus symptom-directed surveillance for brain metastases among patients with triple negative or HER2+ metastatic breast cancer: A pilot study (nct03881605)

2022· article· en· W4220770704 on OpenAlexaff
Katarzyna J. Jerzak, Arjun Sahgal, Gregory R. Pond, Priscilla K. Brastianos, Orit Freedman, Greg J. Stanisz, Ellen Warner

Bibliographic record

VenueCancer Research · 2022
Typearticle
Languageen
FieldMedicine
TopicBrain Metastases and Treatment
Canadian institutionsLakeridge HealthMcMaster UniversityUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineMetastatic breast cancerMagnetic resonance imagingOncologyCancerPopulationInternal medicineBreast cancerQuality of life (healthcare)AnxietyRadiologyPsychiatry

Abstract

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Abstract Background: The incidence of brain metastases (BrM) has steadily increased over time as women with metastatic breast cancer (MBC) live longer and survive to experience spread of cancer to the central nervous system (CNS). Women with triple negative and HER2+ MBC, which represent 30-40% of the MBC population, are at particularly high risk of BrM. At present, MBC patients are not screened for BrM; rather, they undergo imaging of the brain only after symptoms suggestive of CNS involvement develop. Unfortunately, by the time that patients experience potentially debilitating symptoms of BrM, they often have a significant burden of disease with limited treatment options and a poor prognosis. We hypothesize that early detection of BrM via magnetic resonance imaging (MRI)-based screening may allow for early intervention and, ultimately, improved outcomes for MBC patients. Methods: A multi-centre, open-label prospective phase II study, randomizing 50 women with triple negative or HER2+ MBC to either MRI-based BrM screening or symptom-directed surveillance. Randomization is stratified for tumor subtype. Intervention arm: Contrast-enhanced MRI of the brain at baseline, 4-, 8- and 12-months with concurrent chemical exchange saturation transfer (CEST) imaging, a new metabolic MRI sequence that may detect BrM even earlier than standard MRI. Control arm: Symptom-directed surveillance (brain imaging only if patients develop symptoms suggestive of BrM). All participants complete questionnaires at baseline, 6- and 15-months to assess overall (EORTC QLQ BN20) and neurologic-specific (FACT-BR tools) quality of life (QoL) as well as cancer-related anxiety (NCI PRO-CTCAE). A blood sample is collected for ctDNA analysis at baseline in all patients at diagnosis of BrM, if applicable. Key inclusion criteria: 1) Age ≥18; 2) Triple negative MBC, with metastatic disease diagnosed ≤12 weeks prior to study entry OR HER2+ MBC with no restrictions regarding timeline of diagnosis; 3) No symptoms of BrM or known asymptomatic BrM at study entry. Key exclusion criteria: 1) ECOG>2; 2) Inability to complete an MRI (e.g., claustrophobia). Analyses: The primary goal of this study is to determine the feasibility of a randomized trial of BrM screening versus symptom-directed surveillance in the proposed patient population. The pilot study will be considered “not feasible” if <30% of eligible patients enroll, <50% complete the study protocol, and/or >50% of patients allocated to the control arm are screened for BrM with CT or MRI. Overall survival will be assessed as a secondary endpoint. We will also investigate how screening for BrM influences the detection rate of BrM and explore how subsequent intervention affects both overall and neurologic-specific QoL. Our data will enable a power calculation to determine a sample size for a future, larger trial. 28 of 50 planned patients have enrolled in the study to-date. Citation Format: Katarzyna J Jerzak, Arjun Sahgal, Gregory Pond, Priscilla K Brastianos, Orit Freedman, Gregory Stanisz, Ellen Warner. MRI screening versus symptom-directed surveillance for brain metastases among patients with triple negative or HER2+ metastatic breast cancer: A pilot study (nct03881605) [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 OT1-07-01.

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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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.363
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.108
GPT teacher head0.404
Teacher spread0.296 · 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 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

Citations6
Published2022
Admission routes1
Has abstractyes

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