Outcome prediction models incorporating clinical variables for Head and Neck Squamous cell Carcinoma: A systematic review of methodological conduct and risk of bias

Farhannah Aly; Christian Rønn Hansen; Daniel Al Mouiee; Purnima Sundaresan; Ali Haidar; Shalini Vinod; Lois Holloway

doi:10.1016/j.radonc.2023.109629

Outcome prediction models incorporating clinical variables for Head and Neck Squamous cell Carcinoma: A systematic review of methodological conduct and risk of bias

Radiother Oncol. 2023 Jun:183:109629. doi: 10.1016/j.radonc.2023.109629. Epub 2023 Mar 18.

Authors

Farhannah Aly¹, Christian Rønn Hansen², Daniel Al Mouiee³, Purnima Sundaresan⁴, Ali Haidar⁵, Shalini Vinod⁶, Lois Holloway⁷

Affiliations

¹ Ingham Institute for Applied Medical Research, Sydney, Australia; Southwest Sydney Clinical Campus, University of New South Wales, Sydney, Australia; Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia. Electronic address: Farhannah.Aly@health.nsw.gov.au.
² Laboratory of Radiation Physics, Odense University Hospital, Odense, Denmark; Department of Clinical Research, University of Southern Denmark, Odense, Denmark; Danish Centre for Particle Therapy, Aarhus University Hospital, Denmark; Institute of Medical Physics, School of Physics, University of Sydney, Sydney, Australia.
³ Ingham Institute for Applied Medical Research, Sydney, Australia; Southwest Sydney Clinical Campus, University of New South Wales, Sydney, Australia; Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia.
⁴ Sydney West Radiation Oncology Network, Western Sydney Local Health District, Sydney, Australia; Sydney Medical School, The University of Sydney, Sydney, Australia.
⁵ Ingham Institute for Applied Medical Research, Sydney, Australia; Southwest Sydney Clinical Campus, University of New South Wales, Sydney, Australia.
⁶ Southwest Sydney Clinical Campus, University of New South Wales, Sydney, Australia; Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia.
⁷ Ingham Institute for Applied Medical Research, Sydney, Australia; Southwest Sydney Clinical Campus, University of New South Wales, Sydney, Australia; Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia; Institute of Medical Physics, School of Physics, University of Sydney, Sydney, Australia.

PMID: 36934895
DOI: 10.1016/j.radonc.2023.109629

Abstract

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, assess their methodological quality and identify those with potential utility for clinical practice. Inclusion criteria were mucosal HNSCC prognostic prediction model studies (development or validation) incorporating clinically available variables accessible at time of treatment decision making and predicting 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. 64 eligible publications were identified; 55 reported model development, 37 external validations, with 28 reporting both. 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 (3/55 model developments), specifically model intercept, baseline survival or full model code. Most publications (54/55 model developments, 28/37 external validations) were found to have high risk of bias, predominantly due to methodological issues in the PROBAST analysis domain. The identified methodological issues may affect prediction model accuracy in heterogeneous populations. Independent external validation studies in the local population and demonstration of clinical impact are essential for the clinical implementation of outcome prediction models.

Keywords: Head and neck cancer; Methodology; Outcome prediction; Systematic review.

Publication types

Review
Systematic Review

MeSH terms

Bias
Head and Neck Neoplasms*
Humans
Outcome Assessment, Health Care*
Prognosis
Squamous Cell Carcinoma of Head and Neck