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Abstract PO-001: Circulating tumor-tissue modified HPV DNA testing in the clinical workup of patients at risk for HPV-positive oropharynx cancer: The IDEA-HPV Study

2023· article· en· W4386784494 on OpenAlexaboutno aff
Sana Batool, Rosh K.V. Sethi, Annette Wang, Kirsten F. A. A. Dabekaussen, Ann Marie Egloff, C. Fitz, Ravindra Uppaluri, Jennifer Shin, Eleni M. Rettig

Bibliographic record

VenueClinical Cancer Research · 2023
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineOtorhinolaryngologyTonsilInternal medicineCancerProspective cohort studyHead and neck cancerOncologyPathologySurgery

Abstract

fetched live from OpenAlex

Abstract Objectives: While outcomes are favorable for Human Papillomavirus (HPV)-positive oropharyngeal squamous cell carcinomas (OPSCCs), early diagnosis may be beneficial to reduce treatment-related morbidity and mortality. Circulating tumor-tissue-modified viral (TTMV) HPV DNA is detectable in approximately 90% of HPV-positive OPSCC patients and may be a useful diagnostic tool in the clinical evaluation of patients at-risk for the disease. Methods: In this prospective exploratory cohort study, patients presenting to an Otolaryngology-Head and Neck Surgery clinic with unexplained signs or symptoms considered high-risk for HPV-positive OPSCC such as neck mass, tonsillar asymmetry or throat pain were recruited between March 2021 and October 2022. Circulating TTMV-HPV DNA testing was performed by a commercial laboratory (Naveris, Natick MA) and results were shared in real time with the subjects and treating clinicians. Clinicians were surveyed regarding the perceived clinical utility of the test. Medical record abstraction was performed to ascertain subsequent diagnoses. Results: Thirty-nine patients were enrolled. Most subjects were women (N=23, 59%), white (N=32, 82%) and never-smokers (N=20, 51%) with median age of 60 years. The most common presenting sign was tonsil asymmetry (N=24, 62%), while 16 (41%) subjects presented with a neck mass. Circulating TTMV-HPV DNA test results were returned within median 7 days (range, 4-21 days). TTMV-HPV DNA was detected in 2/39 subjects (5%), both subsequently diagnosed with HPV-positive OPSCC. Both were white men in their 70s presenting with a neck mass. One woman with undetectable TTMV-HPV DNA was subsequently diagnosed with HPV-positive OPSCC via excisional neck mass biopsy. Other eventual diagnoses relating to presenting signs and symptoms included 3 HPV-negative head and neck squamous cell carcinomas, 2 branchial cleft cysts, and 4 other malignancies. Treating clinician surveys indicated that while the test impacted clinical management only for 8/38 (21%) subjects, it was perceived as helpful in clinical decision-making for 26/38 (68%) subjects, and as useful for similar future patients in 32/37 (86%) subjects. Conclusion: Plasma TTMV-HPV DNA testing is feasible alongside standard clinical work-up for at-risk patients with particular potential for use as a diagnostic aid for HPV-positive OPSCC. Clinicians should, however, be cognizant of its limitations, and should not consider a negative test result to indicate absence of disease. Further studies to evaluate its utility are warranted. Citation Format: Sana Batool, Rosh K. V. Sethi, Annette Wang, Kirsten Dabekaussen, Ann Marie Egloff, Catherine Delvecchio Fitz, Ravindra Uppaluri, Jennifer Shin, Eleni M. Rettig. Circulating tumor-tissue modified HPV DNA testing in the clinical workup of patients at risk for HPV-positive oropharynx cancer: The IDEA-HPV Study [abstract]. In: Proceedings of the AACR-AHNS Head and Neck Cancer Conference: Innovating through Basic, Clinical, and Translational Research; 2023 Jul 7-8; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2023;29(18_Suppl):Abstract nr PO-001.

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)0.003
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 score0.003
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.406
GPT teacher head0.566
Teacher spread0.160 · 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
Published2023
Admission routes1
Has abstractyes

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