Head and Neck Cancer Predictive Risk Estimator To Determine Control and Therapeutic Outcomes of Radiotherapy (HNC-PREDICTOR): Development, International Multi-Institutional Validation, and Web Implementation of Clinic-Ready Model-Based Risk Stratification for Head and Neck Cancer

Authors

Lisanne V van Dijk
Abdallah Sr Mohamed
Sara Ahmed
Nafiul Nipu
G Elisabeta Marai
Kareem Wahid
Nanna M Sijtsema
Brandon Gunn
Adam S Garden
Amy Moreno
Andrew J Hope
Johannes A Langendijk
Clifton D Fuller

Publication Date

1-1-2023

Journal

European Journal of Cancer

Abstract

Background: Personalised radiotherapy can improve treatment outcomes of patients with head and neck cancer (HNC), where currently a 'one-dose-fits-all' approach is the standard. The aim was to establish individualised outcome prediction based on multi-institutional international 'big-data' to facilitate risk-based stratification of patients with HNC.

Methods: The data of 4611 HNC radiotherapy patients from three academic cancer centres were split into four cohorts: a training (n = 2241), independent test (n = 786), and external validation cohorts 1 (n = 1087) and 2 (n = 497). Tumour- and patient-related clinical variables were considered in a machine learning pipeline to predict overall survival (primary end-point) and local and regional tumour control (secondary end-points); serially, imaging features were considered for optional model improvement. Finally, patients were stratified into high-, intermediate-, and low-risk groups.

Results: Performance score, AJCC8thstage, pack-years, and Age were identified as predictors for overall survival, demonstrating good performance in both the training cohort (c-index = 0.72 [95% CI, 0.66-0.77]) and in all three validation cohorts (c-indices: 0.76 [0.69-0.83], 0.73 [0.68-0.77], and 0.75 [0.68-0.80]). Excellent stratification of patients with HNC into high, intermediate, and low mortality risk was achieved; with 5-year overall survival rates of 17-46% for the high-risk group compared to 92-98% for the low-risk group. The addition of morphological image feature further improved the performance (c-index = 0.73 [0.64-0.81]). These models are integrated in a clinic-ready interactive web interface: https://uic-evl.github.io/hnc-predictor/ CONCLUSIONS: Robust model-based prediction was able to stratify patients with HNC in distinct high, intermediate, and low mortality risk groups. This can effectively be capitalised for personalised radiotherapy, e.g., for tumour radiation dose escalation/de-escalation.

Keywords

Humans, Head and Neck Neoplasms, Prognosis, Risk Assessment, Risk Factors, Treatment Outcome, Head and neck cancer, Overall survival, Machine learning, Image biomarkers, Decision support tool

DOI

10.1016/j.ejca.2022.10.011

PMID

36442460

PMCID

PMC9853413

PubMedCentral® Posted Date

1-20-2023

PubMedCentral® Full Text Version

Author MSS

Published Open-Access

yes