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Updated: Aug 10, 2026
A Model for Perineural Invasion in Head and Neck Squamous Cell Carcinoma
Published on: January 5, 2017
An integrated machine learning-based prognostic model in head and neck cancer using the systemic inflammatory
Anurag Singh1, Sung Jun Ma2, Dukagjin Blakaj2
1Department of Radiation Medicine Roswell Park Comprehensive Cancer Center Elm and Carlton Streets Buffalo, NY 14203. USA.
Objective:
To investigate the prognostic utility of systemic inflammatory response index (SIRI) as a biological readout of stress associated immune modulation in head and neck cancer patients who underwent radiation therapy.
Methods:
Random survival forest machine learning was used to model survival in 568 head and neck cancer patients. SIRI was calculated via pre-treatment bloodwork. Model validation was performed in an external cohort of 345 patients. Baseline financial toxicity (FT) and SIRI were studied in 638 patients.
Results:
Incorporation of SIRI (with performance status and smoking history) into a machine learning model identified three risk-groups that significantly stratified overall survival (p<0.0001,) and these findings were validated in the external validation cohort (p<0.001.) Increasing levels of FT were significantly associated with increasing SIRI levels. (p=0.001.).
Conclusions And Relevance:
An integrated machine learning model using clinical features was successfully developed and externally validated. SIRI was significantly associated with increasing FT. Our findings highlight the potential utility of SIRI as a biological marker of FT in head and neck cancer patients.
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