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Serum tumor biomarkers as a surrogate for radiographic assessment of non-small cell lung cancer.

2023· article· en· W4379280924 on OpenAlexaff
Scott Strum, Mark Vincent, Meghan Gipson, Eric McArthur, Daniel Breadner

Bibliographic record

VenueJournal of Clinical Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicRadiomics and Machine Learning in Medical Imaging
Canadian institutionsLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsMedicineInternal medicineLung cancerSurrogate endpointStage (stratigraphy)Response Evaluation Criteria in Solid TumorsPopulationGastroenterologyCancerOncologyRegimenProgressive diseaseSurgeryDisease

Abstract

fetched live from OpenAlex

e21076 Background: Conventional tumor markers are positioned to serve as a useful adjunct in lung cancer management. However, most studies in this field have been specific to a particular disease stage or treatment regimen. Thus, the purpose of this research was to assess whether three minimally invasive, low-cost serum tumor markers (CEA, CA19-9, and CA-125) held associations with radiographic and clinical outcomes in non-small cell lung cancer (NSCLC) patients who received systemic therapy in a more comprehensive patient population. Methods: This was a single-center retrospective study of NSCLC patients treated between January 2016 and August 2020. Serum tumor markers were statistically analyzed for differences in patients who responded or progressed (RECIST 1.1 or iRECIST criteria), associations with demographic and clinical characteristics, and all-cause mortality in pre-defined populations. Disease response was assessed radiographically using RECIST 1.1 criteria. Results: From 533 NSCLC patients screened, 165 met inclusion criteria. Of these, 50.9% were male and 49.1% female. 69.7% had stage IV disease at baseline. The proportion of patients with an elevated CEA, CA-125, and CA19-9 at baseline were 58.8%, 50.9%, and 30.3%, respectively. A subset of 92 patients had paired tumor markers and radiographic scans, from which median (IQR) fold-change in tumor markers from nadir to progression was 2.13 (IQR 1.24 - 3.02; p < 0.001) for CEA (n = 47), 1.46 (IQR 1.13 - 2.18; p < 0.001) for CA19-9 (n = 46), and 1.53 (IQR 0.96 - 2.12; p < 0.001) for CA-125 (n = 47). Median (IQR) fold-change in tumor markers from baseline to radiographic response was 0.50 (IQR 0.27, 0.95; p < 0.001) for CEA (n = 39), 1.08 (IQR 0.74, 1.61; p = 0.99) for CA19-9 (n = 35), and 0.47 (IQR 0.18, 1.26; p = 0.008) for CA-125 (n = 35). Lastly, an elevated baseline CEA was not associated with a difference in overall survival (HR 1.05, 95% CI 0.63-1.74, p = 0.84) in patients with stage IV disease within the total population. Conclusions: Serum CEA, CA-125, and CA19-9 levels were significantly different than nadir in patients who progressed. CEA and CA-125 were significantly different than baseline in those who responded. These cost-effective tumor markers may serve as an important adjunct to clinical decision making. Analysis within a controlled clinical trial is warranted.

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 score0.002
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.054
GPT teacher head0.490
Teacher spread0.435 · 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
Published2023
Admission routes1
Has abstractyes

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