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Development and External Validation of Integrated Machine Learning-Based Prognostic Model in Oropharyngeal Head and

Anurag K Singh1, Sung Jun Ma2, Dukagjin Blakaj2

  • 1Department of Radiation Medicine, Roswell Park Comprehensive Cancer Center, Elm and Carlton Streets, Buffalo, NY 14203, USA.

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Summary

The systemic inflammation response index (SIRI) can predict outcomes in oropharyngeal cancer patients undergoing radiation therapy. A machine learning model incorporating SIRI, performance status, and smoking history effectively identified patient risk groups for improved survival prediction.

Keywords:
lymphocytemonocyteneutrophiloropharynxsquamous cell carcinoma

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Area of Science:

  • Oncology
  • Biomarkers
  • Machine Learning in Medicine

Background:

  • Head and neck cancer of the oropharynx (HNC-OROP) patients often receive radiation therapy.
  • The systemic inflammation response index (SIRI) has shown prognostic value in HNC-OROP.
  • A parsimonious model for predicting HNC-OROP outcomes is needed.

Purpose of the Study:

  • To evaluate the prognostic utility of SIRI in oropharyngeal cancer patients treated with radiation therapy.
  • To develop and validate a machine learning model integrating SIRI for outcome prediction.

Main Methods:

  • Retrospective cohort study of 568 HNC-OROP patients treated with curative-intent radiation therapy.
  • Systemic inflammation response index (SIRI) calculated from pre-treatment bloodwork.
  • Random survival forest (RSF) machine learning model developed and validated in an external cohort of 421 patients.

Main Results:

  • The integrated machine learning model incorporating SIRI, performance status, and smoking history identified three distinct risk groups.
  • These risk groups significantly stratified overall survival in both the primary (p < 0.0001) and external validation cohorts (p = 0.0019).
  • Progression-free survival was also significantly stratified in the validation cohort (p = 0.0025).

Conclusions:

  • A machine learning model integrating SIRI, performance status, and smoking history provides robust prognostic stratification for oropharyngeal cancer patients.
  • This model was successfully developed and externally validated, demonstrating its clinical relevance.
  • SIRI is a valuable biomarker for predicting outcomes in HNC-OROP patients undergoing radiation therapy.