A failure-type specific risk prediction tool for selection of head-and-neck cancer patients for experimental treatments

Abstract

ObjectivesThe objective of this work was to develop a tool for decision support, providing simultaneous predictions of the risk of loco-regional failure (LRF) and distant metastasis (DM) after definitive treatment for head-and-neck squamous cell carcinoma (HNSCC). Materials and MethodsRetrospective data for 560HNSCC patients were used to generate a multi-endpoint model, combining three cause-specific Cox models (LRF, DM and death with no evidence of disease (death NED)). The model was used to generate risk profiles of patients eligible for/included in a de-intensification study (RTOG 1016) and a dose escalation study (CONTRAST), respectively, to illustrate model predictions versus classic inclusion/exclusion criteria for clinical trials. The model is published as an on-line interactive tool (https://katrin.shinyapps.io/HNSCCmodel/). ResultsThe final model included pre-selected clinical variables (tumor subsite, T stage, N stage, smoking status, age and performance status) and one additional variable (tumor volume). The treatment failure discrimination ability of the developed model was superior of that of UICC staging, 8th edition (AUCLRF=72.7% vs 64.2%, p<0.001 and AUCDM=70.7% vs 58.8%, p<0.001). Using the model for trial inclusion simulation, it was found that 14% of patients eligible for the de-intensification study had>20% risk of tumor relapse. Conversely, 9 of the 15 dose escalation trial participants had LRF risks<20%. ConclusionA multi-endpoint model was generated and published as an on-line interactive tool. Its potential in decision support was illustrated by generating risk profiles for patients eligible for/included in clinical trials for HNSCC.