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Concordance between CA-125 and RECIST progression (PD) in patients with germline BRCA-mutated platinum-sensitive, relapsed ovarian cancer treated with a PARP inhibitor (PARPi) as maintenance therapy after response to chemotherapy.

2020· article· en· W3031086551 on OpenAlexaff
Angelina Tjokrowidjaja, Chee Khoon Lee, Michael Friedländer, Val Gebski, Laurence Gladieff, Jonathan A. Ledermann, Richard T. Penson, Amit M. Oza, Jacob Korach, Tomasz Huzarski, Luís Manso, Carmela Pisano, Rebecca Asher, Nicoletta Colombo, Tjoung‐Won Park‐Simon, Keiichi Fujiwara, Gabe S. Sonke, Ignace Vergote, Jae‐Weon Kim, Éric Pujade-Lauraine

Bibliographic record

VenueJournal of Clinical Oncology · 2020
Typearticle
Languageen
FieldMedicine
TopicPARP inhibition in cancer therapy
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicineResponse Evaluation Criteria in Solid TumorsConcordanceInternal medicineSurrogate endpointOlaparibProgressive diseaseOncologyChemotherapy

Abstract

fetched live from OpenAlex

6014 Background: There are no data to support CA-125 as a surrogate biomarker for ovarian cancer PD in patients on maintenance therapy with a PARPi. We aimed to assess the concordance of PD by CA-125 with RECIST PD in patients treated with maintenance PARPi. Methods: We extracted data on PD as defined by GCIG CA-125 and investigator-assessed RECIST from the SOLO2/ENGOT-Ov21 (NCT01874353) trial. Patients were categorized into: (i) CA-125 and RECIST non-PD concordant; (ii) CA-125 and RECIST PD concordant; and (iii) CA-125 and RECIST discordant. We excluded those with PD other than by RECIST, PD on date of randomization, and no repeat CA-125 beyond baseline. To assess the concordance of CA-125 PD with RECIST PD and CA-125 non-PD with RECIST non-PD, we computed the positive predictive value (PPV), i.e. the probability that patients with CA-125 PD also had RECIST PD, and negative predictive value (NPV), i.e. probability that patients with no CA-125 PD also did not have RECIST PD, respectively. Results: Of 295 randomised patients, 275 (184 olaparib, 91 placebo) were included in the primary analysis. 80 (29%) had CA-125 PD and 77 had concordant RECIST PD, resulting in a PPV of 96% (95% CI 90%-99%). Of 195 patients without CA-125 PD, 101 also did not have RECIST PD, resulting in a NPV of 52% (95% CI 45%-59%; Table). Among those with RECIST PD (n = 171), a greater proportion of patients with RECIST-only PD had a normal baseline CA-125 than those with both CA-125 and RECIST PD (94% vs 69%; p< 0.001). Of 94 patients without CA-125 PD but had RECIST PD, 65 (69%) had CA-125 that remained within normal range, while 27 (29%) had rising and elevated CA-125 that did not meet the criteria for GCIG CA125-PD. Discordance between RECIST PD and CA-125 non-PD was similar in early (≤12 weeks) and late ( > 12 weeks) PD (56% vs 55%, respectively; p= 0.96). Conclusions: Almost half the patients with RECIST PD did not have CA-125 PD and most had CA-125 still within the normal range. Regular imaging should be considered as part of surveillance in patients on maintenance olaparib rather than relying on CA-125 alone. [Table: see text]

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)0.004
metaresearch head score (Gemma)0.013
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 score0.004
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.049
GPT teacher head0.405
Teacher spread0.356 · 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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Citations2
Published2020
Admission routes1
Has abstractyes

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