MétaCan
Menu
← Back to cohort
Record W4414399915 · doi:10.1016/s0167-8140(25)04664-x

AUTOMATED CBCT-BASED TUMOUR VOLUME TRACKING IS PROGNOSTIC IN VIRALLY-MEDIATED HEAD & NECK CANCER

2025· article· en· W4414399915 on OpenAlexaff
Eric Stutheit-Zhao, Tony Tadic, Tirth Patel, Ahmad Bushehri, Hon Biu Chan, Olive Wong, Jenny Lee, Shao Hui Huang, Zeynep Baskurt, B.C. John Cho, Ezra Hahn, Andrew McPartlin, Ali Hosni, John Kim, John Waldron, Andrea McNiven, Benjamin Haibe‐Kains, Andrew Hope, Scott V. Bratman

Bibliographic record

VenueRadiotherapy and Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicRadiomics and Machine Learning in Medical Imaging
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsHead and neck cancerCone beam computed tomographyRadiation therapyChemoradiotherapyHead and neckCancerChemotherapyImage-guided radiation therapy

Abstract

fetched live from OpenAlex

Radiotherapy (RT) or chemoradiotherapy (CRT) enables curative treatment in many head & neck cancer (HNC) patients, but 20-40% experience recurrence. Improved early biomarkers of treatment response could stratify patients for therapy intensification or de-intensification. Cone beam CT (CBCT), routinely acquired for image-guided RT, is underutilized for response assessment. Automated CBCT-based tumour volume tracking may provide a biomarker of response and intrinsic radiosensitivity. We retrospectively evaluated gross primary tumour volume changes (GTVp) during RT/CRT using automated CBCT analysis in patients with nasopharynx (NPC) and oropharynx (OPC) cancer with mature clinical outcomes. We analyzed CBCT images of non-metastatic NPC and OPC treated with curative RT/CRT from 2006–2017. Patients missing >5 CBCT images and OPC with GTVp<20 cc were excluded (the latter due to poor reproducibility observed in CBCT analysis). We used deformable registration to map manually delineated GTVp to CBCTs, with air/bone excluded using Otsu thresholding. Percent change in GTVp per fraction (Fx) was fitted with locally weighted smoothing and the area under the curve (AUC) was computed to summarize both rate and extent of GTVp change. Associations with recurrence-free survival (RFS) were analyzed using multivariable Cox models adjusted for baseline GTVp. We analyzed 23285 CBCT images from 657 patients: 262 NPC (216 EBV+, 17 EBV-, 29 not tested), 395 OPC (271 p16+, 124 p16−). Median follow-up was 6.2 years. GTVp decreased in 93.0% of patients, with median change of -9.97% (range: -59.8, +10.5) by end of treatment. Concurrent chemotherapy was associated with greater GTVp shrinkage (log-likelihood ratio [LR]: -0.50 [-0.91, -0.09], p=0.02). Patients with p16- OPC trended towards less GTVp shrinkage (LR: -0.40 [-0.98, 0.18], p=0.17). GTVp shrinkage was not correlated with smoking history or baseline GTVp. Greater GTVp shrinkage during RT/CRT predicted favourable RFS in NPC (hazard ratio, HR: 1.19 [1.02, 1.39], p=0.020) and p16+ OPC (HR: 1.13 [1.01, 1.28], p=0.036) but not p16− OPC (HR: 1.03 [0.93, 1.14], p=0.579), in a Cox model adjusted for baseline GTVp. Examining earlier treatment response markers, Fx 18 AUC showed the strongest correlation with total AUC (Pearson R=0.916) and was similarly significantly associated with RFS in NPC (HR: 1.45 [1.01, 2.08], p=0.040) and p16+ OPC (HR: 1.45 [1.11, 1.91], p=0.007). In a large retrospective cohort with mature clinical follow-up, automated GTVp tracking on CBCT was prognostic for RFS as early as Fx 18 in virally-mediated HNC treated with definitive RT/CRT. These findings support the further development of CBCT-based biomarkers as a practical biomarker for adaptive risk stratification.

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)0.001
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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
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)0.0010.000

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 head0.015
GPT teacher head0.355
Teacher spread0.340 · 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
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueRadiotherapy and Oncology→Same topicRadiomics and Machine Learning in Medical Imaging→French-language works237,207→