RT Journal Article
T1 Site-Specific Variation in Radiomic Features of Head and Neck Squamous Cell Carcinoma and Its Impact on Machine Learning Models.
A1 Liu, Xiaoyang
A1 Maleki, Farhad
A1 Muthukrishnan, Nikesh
A1 Ovens, Katie
A1 Huang, Shao Hui
A1 Pérez-Lara, Almudena
A1 Romero-Sanchez, Griselda
A1 Bhatnagar, Sahir Rai
A1 Chatterjee, Avishek
A1 Pusztaszeri, Marc Philippe
A1 Spatz, Alan
A1 Batist, Gerald
A1 Payabvash, Seyedmehdi
A1 Haider, Stefan P
A1 Mahajan, Amit
A1 Reinhold, Caroline
A1 Forghani, Behzad
A1 O'Sullivan, Brian
A1 Yu, Eugene
A1 Forghani, Reza
K1 classification
K1 head and neck squamous cell carcinomas
K1 human papilloma virus
K1 machine learning
K1 metastasis
K1 radiomics
AB Current radiomic studies of head and neck squamous cell carcinomas (HNSCC) are typically based on datasets combining tumors from different locations, assuming that the radiomic features are similar based on histopathologic characteristics. However, molecular pathogenesis and treatment in HNSCC substantially vary across different tumor sites. It is not known if a statistical difference exists between radiomic features from different tumor sites and how they affect machine learning model performance in endpoint prediction. To answer these questions, we extracted radiomic features from contrast-enhanced neck computed tomography scans (CTs) of 605 patients with HNSCC originating from the oral cavity, oropharynx, and hypopharynx/larynx. The difference in radiomic features of tumors from these sites was assessed using statistical analyses and Random Forest classifiers on the radiomic features with 10-fold cross-validation to predict tumor sites, nodal metastasis, and HPV status. We found statistically significant differences (p-value ≤ 0.05) between the radiomic features of HNSCC depending on tumor location. We also observed that differences in quantitative features among HNSCC from different locations impact the performance of machine learning models. This suggests that radiomic features may reveal biologic heterogeneity complementary to current gold standard histopathologic evaluation. We recommend considering tumor site in radiomic studies of HNSCC.
SN 2072-6694
YR 2021
FD 2021-07-24
LK http://hdl.handle.net/10668/18314
UL http://hdl.handle.net/10668/18314
LA en
DS RISalud
RD Apr 17, 2025