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Updated: Jul 28, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Predicting oral cancer survival-Development and validation of an Asia-Pacific nomogram
Weilan Wang1, Qingpeng Zhang1, Peter Thomson2
1School of Data Science, The City University of Hong Kong, Hong Kong, China.
Background:
Nomograms are graphical calculating devices that predict response to treatment during cancer management. Oral squamous cell carcinoma (OSCC) is a lethal and deforming disease of rising incidence and global significance. The aim of this study was to develop a nomogram to predict individualized OSCC survival using a population-based dataset obtained from Queensland, Australia and externally validated using a cohort of OSCC patients treated in Hong Kong.
Methods:
Clinico-pathological data for newly diagnosed OSCC patients, including age, sex, tumour site and grading, were accessed retrospectively from the Queensland Cancer Registry (QCR) in Australia and the Clinical Data Analysis and Reporting System (CDARS) in Hong Kong. Multivariate Cox proportional hazard regression was used to construct overall survival (OS) and cancer-specific survival (CSS) prediction models. Nomograms were internally validated using 10-fold cross validation, and externally validated against the Hong Kong dataset.
Results:
Data from 9885 OSCC patients in Queensland and 465 patients from Hong Kong were analysed. All clinico-pathological variables significantly influenced survival outcomes. Nomogram calibration curves demonstrated excellent agreement between predicted and actual probability for Queensland patients. External validation in the Hong Kong population demonstrated slightly poorer nomogram performance, but predictive power remained strong.
Conclusion:
Based upon readily available data documenting patient demographic and clinico-pathological variables, predictive nomograms offer pragmatic aid to clinicians in individualized treatment planning and prognosis assessment in contemporary OSCC management.
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