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Record W4387866093 · doi:10.1158/1078-0432.ccr-23-1013

Developing and Validating a Multivariable Prognostic-Predictive Classifier for Treatment Escalation of Oropharyngeal Squamous Cell Carcinoma: The PREDICTR-OPC Study

2023· article· en· W4387866093 on OpenAlexaff
Hisham Mehanna, Davy Rapozo, Sandra Ventorin von Zeidler, Kevin J. Harrington, Stuart C. Winter, A. Hartley, Paul Nankivell, Andrew Schache, Philip Sloan, Edward Odell, Selvam Thavaraj, Keith D. Hunter, Ketan Shah, Gareth J. Thomas, Anna Long, Rasoul Amel-Kashipaz, Rachel M. Brown, Brendan Conn, Gillian L. Hall, Paul Matthews, Justin Weir, Yen Yeo, Miranda Pring, Catharine West, James McCaul, Paweł Golusiński, Alice Sitch, Rachel Spruce, Nikolaos Batis, J. Bryant, Jill Brooks, Terence M. Jones, Francesca M. Buffa, Syed Haider, Max Robinson

Bibliographic record

VenueClinical Cancer Research · 2023
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsInstitute of Cancer Research
FundersMedical Research CouncilNational Institute for Health and Care ResearchDepartment of Health and Social CareCancer Research UK
KeywordsMedicineInternal medicineOncologyTissue microarrayCohortBiomarkerChemoradiotherapyConfidence intervalRetrospective cohort studyRadiation therapyHead and neck squamous-cell carcinomaHead and neck cancerCancer

Abstract

fetched live from OpenAlex

PURPOSE: While there are several prognostic classifiers, to date, there are no validated predictive models that inform treatment selection for oropharyngeal squamous cell carcinoma (OPSCC).Our aim was to develop clinical and/or biomarker predictive models for patient outcome and treatment escalation for OPSCC. EXPERIMENTAL DESIGN: We retrospectively collated clinical data and samples from a consecutive cohort of OPSCC cases treated with curative intent at ten secondary care centers in United Kingdom and Poland between 1999 and 2012. We constructed tissue microarrays, which were stained and scored for 10 biomarkers. We then undertook multivariable regression of eight clinical parameters and 10 biomarkers on a development cohort of 600 patients. Models were validated on an independent, retrospectively collected, 385-patient cohort. RESULTS: A total of 985 subjects (median follow-up 5.03 years, range: 4.73-5.21 years) were included. The final biomarker classifier, comprising p16 and survivin immunohistochemistry, high-risk human papillomavirus (HPV) DNA in situ hybridization, and tumor-infiltrating lymphocytes, predicted benefit from combined surgery + adjuvant chemo/radiotherapy over primary chemoradiotherapy in the high-risk group [3-year overall survival (OS) 63.1% vs. 41.1%, respectively, HR = 0.32; 95% confidence interval (CI), 0.16-0.65; P = 0.002], but not in the low-risk group (HR = 0.4; 95% CI, 0.14-1.24; P = 0.114). On further adjustment by propensity scores, the adjusted HR in the high-risk group was 0.34, 95% CI = 0.17-0.67, P = 0.002, and in the low-risk group HR was 0.5, 95% CI = 0.1-2.38, P = 0.384. The concordance index was 0.73. CONCLUSIONS: We have developed a prognostic classifier, which also appears to demonstrate moderate predictive ability. External validation in a prospective setting is now underway to confirm this and prepare for clinical adoption.

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)0.015
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.021
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.388
GPT teacher head0.531
Teacher spread0.143 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations14
Published2023
Admission routes1
Has abstractyes

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