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Tumor volume, circulating tumor cells, and cfDNA changes during radiotherapy in patients with head and neck cancer.

2019· article· en· W2946951304 on OpenAlexaff
Sweet Ping Ng, Carolyn Hall, Salyna Meas, Vanessa N. Sarli, Carlos Cárdenas, Houda Bahig, Baher Elgohari, Amy C. Moreno, Heath D. Skinner, Adam S. Garden, D.I. Rosenthal, William H. Morrison, Jack Phan, Steven J. Frank, Clifton D. Fuller, Anthony Lucci

Bibliographic record

VenueJournal of Clinical Oncology · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsCentre Hospitalier de l’Université de MontréalHôpital Notre-Dame
Fundersnot available
KeywordsMedicineCirculating tumor cellHead and neck cancerRadiation therapyCancerHead and neck squamous-cell carcinomaProspective cohort studyInternal medicineOncologyNuclear medicinePathologyMetastasis

Abstract

fetched live from OpenAlex

6062 Background: Head and neck (HN) cancer treatment response relies heavily on macroscopic clinical findings. Monitoring of circulating markers during treatment may improve detection of responders versus non-responders during radiotherapy (RT). Our work prospectively describes the changes in gross tumor volume (GTV), circulating tumor cell (CTC), and cell-free DNA (cfDNA) enumeration during RT. Methods: Patients with intact HN squamous cell carcinoma were enrolled in a prospective IRB-approved study. Pre-, after first RT, weekly in-, and post-RT blood samples were collected. Serial pre-, weekly in-, and post-RT magnetic resonance imaging (MRI) was obtained. Serial GTV measurements were recorded. CTC was enumerated using the FDA-approved CellSearch (Menarini Silicon Biosystems) system. Plasma were collected and cfNA from pre-, mid- and post-RT timepoints were isolated using the MagMAX Nucleic Acid Isolation Kit (Thermo Fisher Scientific), and cfDNA were quantified with Qubit high sensitivity dsDNA assay (Invitrogen). Results: 40 patients were eligible for analysis. Median age was 60 years and 36 were males. The median pre-RT GTV was 14.1cc (range 1.3 – 44.9cc). There was a median reduction of 81% in GTV by week 4 (p < 0.0001). Of the 341 samples analyzed for CTC, 146 (43%) had detectable CTC. 7 patients had detectable pre-RT CTCs (1-3/7.5ml blood). There was no correlation between cancer stage, nodal status, and GTV with detection of CTC. After 1 fraction of RT, 14 patients had CTCs detected, including 11 who had no CTC detected prior. All patients had CTC detected at some point during RT except for 2 patients who had none. In week 4, with significant reduction in GTV, 25 (63%) had detectable CTC. 16 and 11 patients had detectable CTC in final week and post-RT timepoints. The cfDNA levels increased during RT, with its highest level in the final week of RT and lowest at post-RT time-point, inversely correlated with GTV. Conclusions: Our study showed that CTCs can be detected during RT, suggesting mobilization into peripheral circulation during RT with unknown viability. cfDNA kinetics during RT correlated with CTC release, and may indicate apoptotic change during RT. Combined cfDNA-CTC as an early marker of treatment response should be investigated further.

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)0.000
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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.019
GPT teacher head0.334
Teacher spread0.315 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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