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Home > Issues > RetrieveThe Applications of Radiomics for Predicting Survival Rate of Patients with Head and Neck Squamous Cell Carcinoma (HNSCC)
Journal of Magnetics, Volume 25, Number 4, 31 Dec 2020, Pages 587-594
Sang Won Park (Department of Medical Bigdata Convergence, Kangwon National University), Payam Hosseinzadeh Kasani (Department of Neurology, Kangwon National University Hospital), Na Young Yeo (Department of Advanced Research and Development, Hanhwa Hightech Corp.), Gab-Jung Kim (Department of Radiological Technology, Songho University), Se-Jong Yoo (Department of Radiological Technology, Daejeon Health Institute of Technology), Jin Su Kim * 
(Radiologcial and Medico-Oncological Sciences, University of Science and Technology (UST))
(Radiologcial and Medico-Oncological Sciences, University of Science and Technology (UST))
Abstract
The radiomics based on positron emission tomography (PET) data and random forest can predict overall survival
rate of head and neck squamous cell carcinoma (HNSCC). We used the texture features extracted from
PET and clinical information from patients with HNSCC (n = 138). The Spearman's correlation analysis,
Kaplan-Meier log rank test and random forest were used for survival significance and to predict survival rate
of patients with HNSCC. Zone Length Non-Uniformity (ZLNU) was defined as a new key radiomics feature to
predict survival rate. For stage N2 group, predicted survival rate was 76.2 % and actual survival rate was
73.3%. For stage IVA group, predicted survival rate was 74.7 % and actual survival rate was 73.8 %. The
result of this study that applications of 18F-(FDG)-PET images using radiomics features was validated and
could be expected to be used as the basis for future research using MRI images with more distinct structures.
rate of head and neck squamous cell carcinoma (HNSCC). We used the texture features extracted from
PET and clinical information from patients with HNSCC (n = 138). The Spearman's correlation analysis,
Kaplan-Meier log rank test and random forest were used for survival significance and to predict survival rate
of patients with HNSCC. Zone Length Non-Uniformity (ZLNU) was defined as a new key radiomics feature to
predict survival rate. For stage N2 group, predicted survival rate was 76.2 % and actual survival rate was
73.3%. For stage IVA group, predicted survival rate was 74.7 % and actual survival rate was 73.8 %. The
result of this study that applications of 18F-(FDG)-PET images using radiomics features was validated and
could be expected to be used as the basis for future research using MRI images with more distinct structures.
Keywords: HNSCC; radiomics; survival analysis; positron emission tomography; medical image analysis; MRI application
DOI: https://doi.org/10.4283/JMAG.2020.25.4.587
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