Empirical comparison of routinely collected electronic health record data for head and neck cancer‐specific survival in machine‐learnt prognostic models
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 pract...
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Published in | Head & neck Vol. 45; no. 2; pp. 365 - 379 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Hoboken, USA
John Wiley & Sons, Inc
01.02.2023
Wiley Subscription Services, Inc |
Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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Bibliography: | Funding information Australian Government Research Training Program Scholarship Funding information Australian Government Research Training Program Scholarship Section Editor: Benjamin Judson |
ISSN: | 1043-3074 1097-0347 |
DOI: | 10.1002/hed.27241 |