Cancers, Vol. 13, Pages 4559: Machine Learning Incorporating Host Factors for Predicting Survival in Head and Neck Squamous Cell Carcinoma Patients

Cancers, Vol. 13, Pages 4559: Machine Learning Incorporating Host Factors for Predicting Survival in Head and Neck Squamous Cell Carcinoma Patients Cancers doi: 10.3390/cancers13184559 Authors: Han Yu Sung Jun Ma Mark Farrugia Austin J. Iovoli Kimberly E. Wooten Vishal Gupta Ryan P. McSpadden Moni A. Kuriakose Michael R. Markiewicz Jon M. Chan Wesley L. Hicks Mary E. Platek Anurag K. Singh 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 excellen...
Source: Cancers - Category: Cancer & Oncology Authors: Tags: Article Source Type: research