Rapid Fire Oral Presentation Clinical Oncology Society of Australia Annual Scientific Meeting 2026

Towards precision care for older patients with Head and Neck cancer: External validation of a multi-endpoint outcome prediction model in an Australian radiotherapy cohort (145164)

Farhannah Aly 1 2 3 , Joseph Descallar 3 4 , Kristy Robledo 5 , Purnima Sundaresan 6 7 , Alexis A Miller 8 , Shalini Vinod 2 3 4 7 , Lois Holloway 1 2 3 9
  1. Medical Physics Research Group, Ingham Institute For Applied Medical Research, Liverpool, NSW, Australia
  2. Southwest Sydney Clinical Campus, University of New South Wales, Sydney, NSW, Australia
  3. Liverpool and Macarthur Cancer Therapy Centres, Southwest Sydney Local Health District, Sydney, New South Wales, Australia
  4. Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia
  5. NHMRC Clinical Trials Centre, University of Sydney, Sydney, NSW, Australia
  6. Sydney West Radiation Oncology Network, Western Sydney Local Health District, Sydney, NSW, Australia
  7. Sydney Medical School, The University of Sydney, Sydney, NSW, Australia
  8. Illawarra Cancer Care Centre, Illawarra Shoalhaven LHD, Wollongong, NSW, Australia
  9. Institute of Medical Physics, School of Physics, University of Sydney, Sydney, NSW, Australia

Aims: Outcome prediction models may offer a precision approach for clinical decision-making. Prior to clinical use, independent external validation is important to demonstrate accuracy and generalisability across populations. We validated a Danish Head & Neck cancer outcome prediction model in an Australian population.

Methods: Retrospective data from 502 patients treated at six centres were used to validate the multi-endpoint model, predicting 3year locoregional failure (LRF), distant metastases (DM) and death without evidence of disease (DeathNED). Model performance was assessed using Area-Under-Curve (AUC), calibration plots and sub-distribution hazard ratios for competing risks. Model validation was also undertaken in patients ≥70yrs to examine model fairness in this subgroup.

Results: For the full cohort, 3yr AUC values were 0.66 (LRF), 0.73 (DM) and 0.74 (DeathNED), with similar values for the older subgroup. Sub-distribution hazard ratios demonstrated the model could distinguish low (ref), intermediate, and high-risk groups for LRF (p<0.001) (HR 1.92; 95% CI:1.19-3.09; 4.69; 95% CI:2.79-7.88, respectively) and DM (p<0.001) (HR 2.34; 95% CI:1.07-5.14; 7.19; 95% CI:3.45-15.0). For DeathNED, significant separation was observed between low and high-risk groups (HR 2.40, 95% CI: 1.15-5.02), but not between low and intermediate-risk groups (HR 0.66; 95% CI:0.40-1.08). Calibration plots exhibited good calibration for LRF and DM, with slight miscalibration observed in the high-risk group for LRF (over-prediction at risks >30%). For DeathNED, miscalibration was observed, with under-prediction at risks >25%. For the older subgroup, over-prediction at higher risk for LRF was observed, but the model was well-calibrated for DM and DeathNED.

Conclusions: The model demonstrated consistent performance in an Australian cohort relative to the Danish development cohort, highlighting its generalisability. It may support personalised discussions around treatment decision-making, particularly for older patients who may require treatment modifications due to frailty, comorbidities or personal preferences. It represents a step toward clinically deployable decision-support tools in radiation oncology.