Poster Presentation Clinical Oncology Society of Australia Annual Scientific Meeting 2026

Clinical surrogate indicators of recurrent or metastatic disease for outcome assessment of real-world head and neck cancer data (145707)

Ivy Cerelia Valerie 1 2 3 , Farhannah Aly 1 2 3 , Meredith Johnston 3 , Ben Kong 1 4 , Merran Findlay 1 2 5 6 , Joseph Descallar 1 2 , Nasreen Kaadan 2 7 , Gui Xiong 2 7 , Lois Holloway 1 2 3 , Georgina Kennedy 1 2 5
  1. South Western Sydney Clinical School, Faculty of Medicine, UNSW, Sydney, Australia
  2. Ingham Institute for Applied Medical Research, Sydney, Australia
  3. Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia
  4. Department of Medical Oncology Prince of Wales Hospital, Sydney, Australia
  5. Maridulu Budyari Gumal (SPHERE) Cancer Clinical Academic Group, Sydney, Australia
  6. Chris O’Brien Lifehouse, Sydney, Australia
  7. SWSLHD Cancer Services, Sydney, Australia

Aims: Cancer recurrence or other events impacting survival are not readily captured in routinely collected data. Distinguishing a patient cohort by recurrent or metastatic (R/M) status is important in mucosal head and neck cancer (HNC). Extending prior work in other cancers, this study aims to automate the identification of HNC R/M using clinical surrogate indicators validated against electronic health records, enabling indicator use in datasets lacking R/M dates. Methods: Indicators for R/M treatment patterns were derived from Australian eviQ treatment protocols. The population consisted of adults presenting with primary, non-metastatic HNC across all subsites treated within South Western Sydney Local Health District. Automated identification of R/M events using these indicators was compared with the clinician-recorded reference data. This was assessed using the accuracy of event identification and related details such as time lag, cumulative incidence, and whether the relationship between indicators and survival changed over time. The accuracy analyses were repeated using different time-related parameter values. Results: Twelve indicators of HNC R/M treatment derived from eviQ protocols and clinical expertise were applied in the analysis. Of 862 patients, the indicators achieved an accuracy of 91.5% (95%CI 89.4%-93.3%). Among patients correctly identified as having R/M, the estimated date matched exactly for 66.2% of patients, and a median difference of 65 days (IQR 27-154) in the remaining cases. The cumulative incidence of R/M disease was similar for indicator and reference at 2 years (12.5% vs 8.3%) and 5 years (19.5% vs 15.8%). Patients identified as having R/M by the indicators (7.78; 95%CI 6.11-9.92 vs 7.31; 5.60-9.55) had a similar risk of death over time. The accuracy remained stable across evidence-based scenarios. Conclusions: The surrogate indicators have the potential to estimate R/M events in datasets where R/M dates are missing. This could enable more robust population-level outcome tracking and support health system strategy.

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