ESTRO 2023 - Abstract Book

S990

Digital Posters

ESTRO 2023

Department of Radiation Oncology, Liverpool, Australia; 4 Odense University Hospital, Laboratory of Radiation Physics, Odense, Denmark; 5 University of Southern Denmark, Department of Clinical Research, Odense, Denmark; 6 Aarhus University Hospital, Danish Centre for Particle Therapy, Aarhus , Denmark; 7 Institute of Medical Physics, University of Sydney, School of Physics, Sydney, Australia; 8 University of New South Wales , Southwest Sydney Clinical Campus, Sydney, Australia; 9 Liverpool and Macarthur Cancer Therapy Centres , Department of Radiation Oncology, Liverpool, Australia; 10 Western Sydney Local Health District, Sydney West Radiation Oncology Network, Sydney, Australia; 11 The University of Sydney, Sydney Medical School, Sydney, Australia; 12 Liverpool and Macarthur Cancer Therapy Centres, Department of Radiation Oncology , Liverpool, Australia; 13 Ingham Institute for Applied Medical Research, Medical Physics Research Group , Liverpool, Australia; 14 University of New South Wales, Southwest Sydney Clinical Campus , Sydney, Australia Purpose or Objective Multiple outcome prediction models have been developed for Head and Neck Squamous Cell Carcinoma (HNSCC). This systematic review aimed to identify HNSCC outcome prediction model studies that can be used at time of treatment decision making, and assess their methodological quality, thus making recommendations for clinical practice. Materials and Methods Inclusion criteria were mucosal HNSCC prognostic prediction model studies (development and/or validation), published between 2000-2022, that incorporated clinically available variables and predicted tumour-related outcomes. Eligible publications were identified from PubMed and Embase. Methodological quality and risk of bias were assessed using the checklist for critical appraisal and data extraction for systematic reviews of prediction modelling studies (CHARMS) and prediction model risk of bias assessment tool (PROBAST). Eligible publications were categorised by study type for reporting. Results 64 eligible publications were identified, 55 publications reported model developments and 37 reported external validations. 28 publications reported both model development and external validation. Publication characteristics are shown in table 1. CHARMS checklist items relating to participants, predictors, outcomes, handling of missing data, and some model development and evaluation procedures were generally well-reported. Less well-reported were measures accounting for model overfitting and model performance measures, especially model calibration. Full model information was poorly reported (three of 55 model developments), specifically model intercept, baseline survival or full model code. Most publications (54 of 55 model developments, 28 of 37 model external validations) were found to be at high risk of bias, predominantly due to methodological issues in the PROBAST participants and analysis domains (Figure 1). Factors contributing to high risk of bias included low sample size, inappropriate categorisation of continuous predictors, excluding participants in the analysis, inappropriate handling of participants with missing data, selecting predictors based on univariable analysis and not appropriately accounting for overfitting, underfitting and optimism in model performance.