Singh Anurag K, Ma Sung Jun, Blakaj Dukagjin, Zhu Simeng, Almeida Neil D, Koempel Andrew, Yuan Guangwei, Wang Grace, Wooten Kimberly, Gupta Vishal, McSpadden Ryan, Kuriakose Moni A, Markiewicz Michael R, Yao Song, Hicks Wesley L, Seshadri Mukund, Repasky Elizabeth A, Bouchard Elizabeth G, Farrugia Mark K, Yu Han
Department of Radiation Medicine Roswell Park Comprehensive Cancer Center Elm and Carlton Streets Buffalo, NY 14203. USA.
Department of Radiation Oncology, The Arthur G. James Cancer Hospital and Richard J. Solove Research Institute, The Ohio State University Comprehensive Cancer Center, 460 West 10th Avenue, Columbus, OH, 43210, USA.
Res Sq. 2025 May 7:rs.3.rs-6529613. doi: 10.21203/rs.3.rs-6529613/v1.
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.
研究全身炎症反应指数（SIRI）作为接受放射治疗的头颈癌患者应激相关免疫调节生物学指标的预后价值。
采用随机生存森林机器学习方法对568名头颈癌患者的生存情况进行建模。通过治疗前血液检查计算SIRI。在345例患者的外部队列中进行模型验证。对638例患者的基线财务毒性（FT）和SIRI进行研究。
将SIRI（结合性能状态和吸烟史）纳入机器学习模型，确定了三个风险组，这些风险组显著分层了总生存期（p<0.0001），并且这些发现在外验证队列中得到了验证（p<0.001）。FT水平升高与SIRI水平升高显著相关（p=0.001）。
成功开发并外部验证了一个使用临床特征的综合机器学习模型。SIRI与FT增加显著相关。我们的研究结果突出了SIRI作为头颈癌患者FT生物学标志物的潜在价值。
