Empirical comparison of routinely collected electronic health record data for head and neck cancer‐specific survival in machine‐learnt prognostic models. Issue 2 (11th November 2022)

• Record Type: Journal Article
• Authors: Kotevski, Damian P.; Smee, Robert I.; Vajdic, Claire M.; Field, Matthew
• Is Part Of: Head & neck. Volume 45:Issue 2(2023)
• Page Start: 365
• Page End: 379
• Publication Date: 2022-11-11
• Subjects: cancer‐specific survival -- head and neck cancer -- machine learning -- prediction models -- prognostic factors; Head -- Diseases -- Periodicals; Neck -- Diseases -- Periodicals; Head -- Periodicals; Neck -- Periodicals; Face -- Periodicals; 617.51059
• DOI: 10.1002/hed.27241
• Languages: English
• ISSNs: 1043-3074

Abstract

Abstract: Background: Knowledge of the prognostic factors and performance of machine learning predictive models for 2‐year cancer‐specific survival (CSS) is limited in the head and neck cancer (HNC) population. Methods: Data from our facilities' oncology information system (OIS) collected for routine practice (OIS dataset, n = 430 patients) and research purposes (research dataset, n = 529 patients) were extracted on adults diagnosed between 2000 and 2017 with squamous cell carcinoma of the head and neck. Results: Machine learning demonstrated excellent performance (area under the curve, AUC) in the whole cohort (AUC = 0.97, research dataset), larynx cohort (AUC = 0.98, both datasets), and oropharynx cohort (AUC = 0.99, both datasets). Tumor site and T classification were identified as predictors of 2‐year CSS in both datasets. Hypothyroidism and fitness for operation were further identified in the research dataset. Conclusions: Datasets extracted from an OIS for routine clinical practice and research purposes demonstrated high utility for informing 2‐year head and neck CSS.