Abstract
Simple SummaryAmong head and neck squamous cell carcinoma patients, the five-year survival rates have seen little improvement over the past decade. Prediction of a cancer patient’s clinical outcome is challenging but important for patient counseling and treatment planning. In this work, we evaluated common machine learning models in predicting head and neck squamous cell carcinoma patients’ overall survival based on clinical, demographic features and host factors. We identified the top-performing model and verified host factors can improve the model performance when proper methods are applied. The findings are of critical importance for improved risk stratification of head and neck squamous cell carcinoma patients and provide targeted supportive care for patients who are likely to have the worst outcome.Prognostication for cancer patients is integral for patient counseling and treatment planning, yet providing accurate prediction can be challenging using existing patient-specific clinical indicators and host factors. In this work, we evaluated common machine learning models in predicting head and neck squamous cell carcinoma (HNSCC) patients’ overall survival based on demographic, clinical features and host factors. We found random survival forest had best performance among the models evaluated, which achieved a C-index of 0.729 and AUROC of 0.792 in predicting two-year overall survival. In addition, we verified that host factors are independently predictive of HNSCC overall survival, which improved the C-index by a margin of 0.026 and the AUROC by 0.034. Due to the strong correlation among host factors, we showed that proper dimension reduction is an important step before their incorporation into the machine learning models, which provides a host factor score reflecting the patients’ nutrition and inflammation status. The score by itself showed excellent discriminating capacity with the high-risk group having a hazard ratio of 3.76 (1.93–7.32, p < 0.0001) over the low-risk group. The hazard ratios were further improved to 7.41 (3.66–14.98, p < 0.0001) by the random survival forest model after including demographic and clinical features.
Highlights
Among patients with head and neck squamous cell carcinoma (HNSCC), 5-year survival rates have seen little improvement over the past decade, and, except for HPVassociated oropharyngeal cancers, remain below 50% for locally advanced disease [1]
To efficiently utilize the host factor information, we proposed the application of principal component analysis (PCA)
We evaluated common machine learning models in predicting the head HNSCC patients’ overall survival based on clinical, demographic features and host factors
Summary
Among patients with head and neck squamous cell carcinoma (HNSCC), 5-year survival rates have seen little improvement over the past decade, and, except for HPVassociated oropharyngeal cancers, remain below 50% for locally advanced disease [1]. The current treatment approach is to treat advanced cancers with multimodal therapies. This approach, carries significant complication rates and comorbidities. In addition to well-known factors such as performance status and disease stage, prior work has demonstrated that patient-specific variables and host factors influence HNSCC patient survival. The host factors can reflect the patient’s immune, inflammation and nutritional status. Recent evidence suggests that pretreatment values of neutrophils, monocytes, lymphocytes, hemoglobin and albumin, are independently associated with prognosis in patients with HNSCC [5]. While the findings were important, these factors need to be validated using multivariate methods that consider multiple clinical factors in practicable decision models
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
