# An integrated machine learning-based prognostic model in head and neck cancer using the systemic inflammatory response index and correlations with patient reported financial toxicity

## TL;DR

This study shows that a biological marker called SIRI can predict survival in head and neck cancer patients and is linked to financial stress.

## Contribution

A new machine learning model combining SIRI and clinical data was developed and validated for predicting survival in head and neck cancer.

## Key findings

- A machine learning model using SIRI and clinical features identified three survival risk groups in head and neck cancer patients.
- The model was externally validated and showed significant survival stratification.
- Higher financial toxicity was significantly associated with higher SIRI levels.

## Abstract

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.

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.

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.)

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.