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Prediction of treatment outcome using MRI radiomics and machine learning in oropharyngeal cancer patients after surgical treatment

Authors
 Young Min Park  ;  Jae Yol Lim  ;  Yoon Woo Koh  ;  Se-Heon Kim  ;  Eun Chang Choi 
Citation
 ORAL ONCOLOGY, Vol.122 : 105559, 2021-11 
Journal Title
ORAL ONCOLOGY
ISSN
 1368-8375 
Issue Date
2021-11
MeSH
Humans ; Machine Learning ; Magnetic Resonance Imaging ; Neoplasm Recurrence, Local* / diagnostic imaging ; Oropharyngeal Neoplasms* / diagnostic imaging ; Oropharyngeal Neoplasms* / surgery ; Retrospective Studies ; Treatment Outcome
Keywords
MRI ; Machine learning ; Prediction ; Radiomics ; Treatment outcome
Abstract
Objectives: In this study, we aimed to analyze preoperative MRI images of oropharyngeal cancer patients who underwent surgical treatment, extracted radiomics features, and constructed a disease recurrence and death prediction model using radiomics features and machine-learning techniques.

Materials and methods: A total of 157 patients participated in this study, and 107 stable radiomics features were selected and used for constructing a predictive model.

Results: The performance of the combined model (clinical and radiomics) yielded the following results: AUC of 0.786, accuracy of 0.854, precision of 0.429, recall of 0.500, and f1 score of 0.462. The combined model showed better performance than either the clinical and radiomics only models for predicting disease recurrence. For predicting death, the combined model performance has an AUC of 0.841, accuracy of 0.771, precision of 0.308, recall of 0.667, and f1 score of 0.421. The combined model showed superior performance over the predictive model using only clinical variables. A Cox proportional hazard model using the combined variables for predicting patient death yielded a c-index value that was significantly better than that of the model including only clinical variables.

Conclusions: A predictive model using clinical variables and MRI radiomics features showed excellent performance in predicting disease recurrence and death in oropharyngeal cancer patients. In the future, a multicenter study is necessary to verify the model's performance and confirm its clinical usefulness.
Full Text
https://www.sciencedirect.com/science/article/pii/S1368837521006667?via%3Dihub
DOI
10.1016/j.oraloncology.2021.105559
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Otorhinolaryngology (이비인후과학교실) > 1. Journal Papers
Yonsei Authors
Koh, Yoon Woo(고윤우)
Kim, Se Heon(김세헌)
Park, Young Min(박영민) ORCID logo https://orcid.org/0000-0002-7593-8461
Lim, Jae Yol(임재열) ORCID logo https://orcid.org/0000-0002-9757-6414
Choi, Eun Chang(최은창)
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/187594
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