Development and External Validation of Integrated Machine Learning-Based Prognostic Model in Oropharyngeal Head and Neck Cancer Using the Systemic Inflammatory Response Index

<b>Importance:</b> Patient with head and neck cancer of the oropharynx (HNC-OROP) undergo curative-intent definitive or post-operative radiation therapy. The systemic inflammation response index (SIRI) has independent prognostic capacity in HNC-OROP. We hypothesized that the use of SIRI may produce a parsimonious model of HNC-OROP outcomes. <b>Objective:</b> We aimed to investigate the prognostic utility of systemic inflammatory response index (SIRI) in oropharyngeal head and neck cancer patients who underwent radiation therapy. Design, Setting, and Participants: Random survival forest (RSF) machine learning was used to model survival in 568 oropharyngeal cancer patients in this retrospective cohort study. SIRI was calculated via pre-treatment bloodwork. Model validation was performed in an external cohort of 421 oropharyngeal cancer patients. <b>Exposures:</b> Exposure was curative-intent definitive or post-operative radiation therapy for head and neck cancer of the oropharynx (HNC-OROP). <b>Results:</b> This is a retrospective study with 568 and 421 patients in the Roswell Park and external Ohio State University cohorts. We evaluated full and reduced RSF models and a robust decision tree model. The C-index of the models was 0.758 (RSF full), 0.725 (RSF reduced), and 0.702 (decision tree). The incorporation of SIRI (with performance status and smoking history) into a machine learning model identified three risk-groups that significantly stratified overall survival (<i>p</i> < 0.0001). These findings were validated in the external validation cohort (<i>p</i> = 0.0019). Progression-free survival was also significantly different for the three groups in the validation cohort (<i>p</i> = 0.0025). <b>Conclusions and Relevance:</b> An integrated machine learning model using SIRI, performance status, and smoking history was successfully developed and externally validated in oropharyngeal head and neck cancer patients.

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Journal
Cancers
Published
2025-11-28
DOI
https://doi.org/10.3390/cancers17233820
Primary Topic
Inflammatory Biomarkers in Disease Prognosis
Type
article
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article

Development and External Validation of Integrated Machine Learning-Based Prognostic Model in Oropharyngeal Head and Neck Cancer Using the Systemic Inflammatory Response Index

Song Yao, Vishal Gupta, Sung Jun, Han Yu et al.
Cancers
Inflammatory Biomarkers in Disease Prognosis
article

Development and External Validation of Integrated Machine Learning-Based Prognostic Model in Oropharyngeal Head and Neck Cancer Using the Systemic Inflammatory Response Index

Song Yao, Vishal Gupta, Sung Jun, Han Yu, Elizabeth A. Repasky, Kimberly Wooten, Ryan McSpadden, Michael R. Markiewicz, Mark Farrugia, Dukagjin M. Blakaj, Elizabeth Bouchard, Guangwei Yuan, Wesley L. Hicks, Neil D. Almeida, Simeng Zhu, Moni Abraham Kuriakose, Mukund Seshadri, Anurag K. Singh, Grace Wang, Andrew Koempel
article en

Abstract

<b>Importance:</b> Patient with head and neck cancer of the oropharynx (HNC-OROP) undergo curative-intent definitive or post-operative radiation therapy. The systemic inflammation response index (SIRI) has independent prognostic capacity in HNC-OROP. We hypothesized that the use of SIRI may produce a parsimonious model of HNC-OROP outcomes. <b>Objective:</b> We aimed to investigate the prognostic utility of systemic inflammatory response index (SIRI) in oropharyngeal head and neck cancer patients who underwent radiation therapy. Design, Setting, and Participants: Random survival forest (RSF) machine learning was used to model survival in 568 oropharyngeal cancer patients in this retrospective cohort study. SIRI was calculated via pre-treatment bloodwork. Model validation was performed in an external cohort of 421 oropharyngeal cancer patients. <b>Exposures:</b> Exposure was curative-intent definitive or post-operative radiation therapy for head and neck cancer of the oropharynx (HNC-OROP). <b>Results:</b> This is a retrospective study with 568 and 421 patients in the Roswell Park and external Ohio State University cohorts. We evaluated full and reduced RSF models and a robust decision tree model. The C-index of the models was 0.758 (RSF full), 0.725 (RSF reduced), and 0.702 (decision tree). The incorporation of SIRI (with performance status and smoking history) into a machine learning model identified three risk-groups that significantly stratified overall survival (<i>p</i> < 0.0001). These findings were validated in the external validation cohort (<i>p</i> = 0.0019). Progression-free survival was also significantly different for the three groups in the validation cohort (<i>p</i> = 0.0025). <b>Conclusions and Relevance:</b> An integrated machine learning model using SIRI, performance status, and smoking history was successfully developed and externally validated in oropharyngeal head and neck cancer patients.

CancersVol. 17(23)
Roswell Park Comprehensive Cancer Center (US), The Ohio State University Comprehensive Cancer Center – Arthur G. James Cancer Hospital and Richard J. Solove Research Institute (US), University at Buffalo, State University of New York (US)
Openalex Percentile: Top 47%
Inflammatory Biomarkers in Disease Prognosis
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