Background: The limitations of the traditional TNM system have spurred interest in multivariable models for personalized prognostication in laryngeal and hypopharyngeal cancers (LSCC/HPSCC). However, the performance of these models depends on the quality of data and modelling methodology, affecting their potential for clinical adoption. This systematic review and meta-analysis (SR-MA) evaluated clinical predictive models (CPMs) for recurrence and survival in treated LSCC/HPSCC. We assessed models’ characteristics and methodologies, as well as performance, risk of bias (RoB), and applicability.Methods: Literature searches were conducted in MEDLINE (OVID), Embase (OVID) and IEEE databases from January 2005 to November 2023. The search algorithm used comprehensive text word and index term combinations without language or publication type restrictions. Independent reviewers screened titles and abstracts using a predefined Population, Index, Comparator, Outcomes, Timing and Setting (PICOTS) framework. We included externally validated (EV) multivariable models, with at least one clinical predictor, that provided recurrence or survival predictions. The SR-MA followed PRISMA reporting guidelines, and PROBAST framework for RoB assessment. Model discrimination was assessed using C-index/AUC, and was presented for all models using forest plots. MA was only performed for models that were externally validated in two or more cohorts, using random-effects model. The main outcomes were model discrimination and calibration measures for survival (OS) and/or local recurrence (LR) prediction. All measures and assessments were preplanned prior to data collection.Results: The SR-MA identified 11 models, reported in 16 studies. Seven models for OS showed good discrimination on development, with only one excelling (C-index >0.9), and three had weak or poor discrimination. Inclusion of a radiomics score as a model parameter achieved relatively better performance. Most models had poor generalisability, demonstrated by worse discrimination performance on EV, but they still outperformed the TNM system. Only two models met the criteria for MA, with pooled EV AUCs 0.73 (95% CI 0.71-0.76) and 0.67 (95% CI 0.6-0.74). RoB was high for all models, particularly in the analysis domain.Conclusions: This review highlighted the shortcomings of currently available models, while emphasizing the need for rigorous independent evaluations. Despite the proliferation of models, most exhibited methodological limitations and bias. Currently, no models can confidently be recommended for routine clinical use.Systematic review registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021248762, identifier CRD42021248762.

Objectives This systematic review aims to evaluate externally validated models for individualised prediction of recurrence or survival in adults treated with curative intent for oropharyngeal cancer. Design Systematic review. Setting Hospital care. Methods Systematic searches were conducted up to September 2023 and records were screened independently by at least two reviewers. The Prediction model Risk Of Bias ASsessment Tool was used to assess risk of bias (RoB). Model discrimination measures (c-indices) were presented in forest plots. Clinical and methodological heterogeneity precluded meta-analysis. Results Fifteen studies developing and/or evaluating 25 individualised risk prediction models were included. The majority (77%) of c-indices for model developments and validations were ≥0.7 indicating ‘good’ discriminatory ability for models predicting overall survival. For disease-specific measures, most (73%) c-indices for model development were also ≥0.7, but fewer (40%) were ≥0.7 for external validations. Comparisons across models and outcome measures were hampered by heterogeneity. Only two studies directly compared models in the same cohort. Since all models were subject to a high RoB, primarily due to concerns with the analysis, the trustworthiness of the findings remains uncertain. Concerns included a lack of accounting for potentially missing data, model overfitting or competing risks as well as small event numbers. There were fewer concerns related to the participant, predictor and outcome domains, although reporting was not always detailed enough to make an informed decision. Where human papilloma virus (HPV) status and/or a radiomics score were included as a variable, models had better discriminative ability. Conclusions There were no models assessed as being at low RoB. Given that HPV status or a radiomics score appeared to improve model discriminative performance, further external validation of existing models to assess generalisability should focus on models that include HPV status as a variable. Development and validation of future models should be considered in HPV+ or HPV− cohorts separately to ensure representativeness. PROSPERO registration number CRD42021248762.

The Head and Neck Cancer International Group (HNCIG) has undertaken an international modified Delphi process to reach consensus on the essential data variables to be included in a minimum database for HNC research. Endorsed by 19 research organisations representing 34 countries, these recommendations provide the framework to facilitate and harmonise data collection and sharing for HNC research. These variables have also been incorporated into a ready to use downloadable HNCIG minimum database, available from the HNCIG website.