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Impact of stereotactic ablative radiotherapy (SABR) on detection of ctDNA in patients with early-stage lung cancer: Interim findings from the prospective SABR-DETECT trial.

2025· article· en· W4410812941 on OpenAlexaff
Saurav Verma, Sympascho Young, Thomas Kennedy, Morgan Black, Britney Messam, Emma Churchman, Joanna Laba, George Rodrigues, Yee Ung, May Tsao, Christopher B. Goodman, Melody Qu, Pencilla Lang, Brian Yaremko, Andrew Warner, Ningyou Li, Ruoying Yu, Alexander V. Louie, David A. Palma, Daniel Breadner

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Diagnosis and Treatment
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreWestern University
Fundersnot available
KeywordsSABR volatility modelMedicineAblative caseRadiation therapyInterim analysisStage (stratigraphy)InterimLung cancerRadiologyNuclear medicineOncologySurgeryRandomized controlled trial

Abstract

fetched live from OpenAlex

3044 Background: Stereotactic ablative radiotherapy (SABR) is the preferred curative treatment for inoperable patients with stage I/IIA non–small-cell lung cancer (NSCLC). In cases where the tumor is inaccessible or biopsy carries a high risk of complications, SABR is offered even in the absence of a tissue diagnosis, based on a high likelihood of malignancy as calculated by validated predictive models. In these situations, a blood based liquid biopsy detecting circulating tumor DNA (ctDNA) can serve as an aide to confirm malignancy and allow molecular testing. However, low ctDNA yield in early stage NSCLC presents a challenge for diagnosis. This study hypothesizes that ctDNA detection rates will improve by combining assessment of pre- and post-SABR plasma samples. Methods: This is a multi-institutional study including two cohorts: 1) patients with suspected stage I/IIA NSCLC, with a pretreatment likelihood of malignancy of ≥60% on Herder or Brock models, and 2) patients with biopsy-proven NSCLC. SABR was delivered according to standard guidelines. Plasma was collected for ctDNA analysis before and 24-72 hours following the first fraction of SABR. SHIELDING ULTRA MRD panel of hotspot regions in 2365 cancer-related genes with ultra-high sensitivity was used for ctDNA analysis (mutation + fragment profile + CNV). In this pre-planned interim analysis, we report on the secondary objective: to assess the impact of SABR on detection rates of ctDNA. Results: Paired plasma samples (pre- and post-SABR) were tested for 69 patients. After quality control analysis, 66 paired samples were analyzed and included in this interim analysis. The median age was 76 years (range, 56-89) and 36 (54%) were male. The median concentration of circulating free DNA (ng/mL) did not increase from pre- (5.5, inter quartile range (IQR): 3.3-8.1) to post-SABR (5.7, IQR: 4.1-7.6) (P=0.82). The ctDNA detection rate in pre-SABR samples was 22.7% versus 27.3% in post-SABR samples (Table). Interestingly, in 10 patients (15.2%), ctDNA became detectable in post-SABR samples and in 7 patients (10.6%) the ctDNA was no longer detectable in the post-SABR samples. The ctDNA remained undetectable in 41 patients (62.1%). 37.9% of patients had detectable ctDNA either before or after SABR. Conclusions: The diagnostic yield of ctDNA for confirming malignancy in early stage NSCLC is improved by testing both the pre- and the post-SABR samples, collected within 24-72 hours after the first fraction of SABR. This approach may improve the diagnostic rates of liquid biopsies for patients with presumed NSCLC undergoing SABR, warranting further investigation of ctDNA detection before and shortly after treatment. Clinical trial information: NCT05921474 . ctDNA detection rates (N=66). Pre-SABR Post-SABR n (%) detected detected 8 (12.1%) not detected detected 10 (15.2%) detected not detected 7 (10.6%) not detected not detected 41 (62.1%)

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.040
GPT teacher head0.461
Teacher spread0.420 · 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
Published2025
Admission routes1
Has abstractyes

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