0 15

Cited 0 times in

Machine Learning Based Radiomic HPV Phenotyping of Oropharyngeal SCC: A Feasibility Study Using MRI

 Beomseok Sohn  ;  Yoon Seong Choi  ;  Sung Soo Ahn  ;  Hwiyoung Kim  ;  Kyunghwa Han  ;  Seung-Koo Lee  ;  Jinna Kim 
 LARYNGOSCOPE, Vol.131(3) : E851-E856, 2021-03 
Journal Title
Issue Date
Aged ; Alphapapillomavirus / isolation & purification* ; Feasibility Studies ; Female ; Humans ; Logistic Models ; Machine Learning* ; Magnetic Resonance Imaging* ; Male ; Middle Aged ; Oropharyngeal Neoplasms / diagnostic imaging ; Oropharyngeal Neoplasms / pathology ; Oropharyngeal Neoplasms / virology* ; Papillomavirus Infections / diagnosis* ; Predictive Value of Tests ; ROC Curve ; Retrospective Studies ; Squamous Cell Carcinoma of Head and Neck / diagnostic imaging ; Squamous Cell Carcinoma of Head and Neck / pathology ; Squamous Cell Carcinoma of Head and Neck / virology*
Radiomics ; MRI ; oropharyngeal cancer ; squamous cell carcinoma ; human papillomavirus
Objectives: To investigate whether a radiomic MRI feature-based prediction model can differentiate oropharyngeal squamous cell carcinoma (SCC) according to the human papillomavirus (HPV) status. Study design: Retrospective cohort study. Methods: Pretreatment MRI data from 62 consecutive patients with oropharyngeal SCC were retrospectively reviewed, and chronologically allocated to training (n = 43) and test sets (n = 19). Enhancing tumors were semi-automatically segmented on each slice of the postcontrast T1WI to span the entire tumor volume, after registration of T2WI to postcontrast T1WI; 170 radiomic features were extracted from the entire tumor volume. Relevant features were selected and radiomics models were trained using least absolute shrinkage and selection operator (LASSO) logistic regression model with 10-fold cross-validation, after subsampling of training sets using synthetic minority over-sampling technique to mitigate data imbalance. The selected features, weighted by their respective coefficients, were combined linearly to yield a radiomics score. The diagnostic performance of the radiomic score was evaluated using the area under the receiver operating characteristic curve (AUC). Results: Six radiomic features, which revealed strong association with HPV status of oropharyngeal SCC, were selected using LASSO. The radiomics model yielded excellent performance on the training set (AUC, 0.982 [95% CI, 0.942-1.000]) and moderate performance on the test set (AUC, 0.744 [95% CI, 0.496-0.991]) for differentiating oropharyngeal SCC according to HPV status. Conclusions: Radiomics-based MRI phenotyping differentiates oropharyngeal SCC according to HPV status, and thus, is a potential imaging biomarker. Level of evidence: 3 Laryngoscope, 131:E851-E856, 2021.
Full Text
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Biomedical Systems Informatics (의생명시스템정보학교실) > 1. Journal Papers
Yonsei Authors
Kim, Jinna(김진아) ORCID logo https://orcid.org/0000-0002-9978-4356
Kim, Hwiyoung(김휘영)
Sohn, Beomseok(손범석) ORCID logo https://orcid.org/0000-0002-6765-8056
Ahn, Sung Soo(안성수) ORCID logo https://orcid.org/0000-0002-0503-5558
Lee, Seung Koo(이승구) ORCID logo https://orcid.org/0000-0001-5646-4072
Choi, Yoon Seong(최윤성)
사서에게 알리기


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.