Generic placeholder image

Current Medical Imaging


ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

Research Article

FDG-PET/CT Radiomics Models for The Early Prediction of Locoregional Recurrence in Head and Neck Cancer

Author(s): Hu Cong, Wang Peng, Zhou Tian, Martin Vallières, Xu Chuanpei, Zhu Aijun* and Zhang Benxin

Volume 17 , Issue 3 , 2021

Published on: 12 July, 2020

Page: [374 - 383] Pages: 10

DOI: 10.2174/1573405616666200712181135

Price: $65


Purpose: Both CT and PET radiomics is considered as a potential prognostic biomarker in head and neck cancer. This study investigates the value of fused pre-treatment functional imaging (18F-FDG PET/CT) radiomics for modeling of local recurrence of head and neck cancers.

Materials and Methods: Firstly, 298 patients have been divided into a training set (n = 192) and verification set (n = 106). Secondly, PETs and CTs are fused based on wavelet transform. Thirdly, radiomics features are extracted from the 3D tumor area from PETCT fusion. The training set is used to select the features reduction and predict local recurrence, and the random forest prediction models combining radiomics and clinical variables are constructed. Finally, the ROC curve and KM analysis are used to evaluate the prediction efficiency of the model on the validation set.

Results: Two PET/CT fusion radiomics features and three clinic parameters are extracted to construct the radiomics model. AUC value in the verification set 0.70 is better than no fused sets 0.69. The accuracy of 0.66 is not the highest value (0.67). Either consistency index CI 0.70 (from 0.67 to 0.70) or the p-value 0.025 (from 0.03 to 0.025) get the best result in all four models.

Conclusion: The radiomics model based on the fusion of PETCT is better than the model based on PET or CT alone in predicting local recurrence, the inclusion of clinical parameters may result in more accurate predictions, which has certain guiding significance for the development of personalized, precise treatment scheme.

Keywords: Local Recurrence, outcome prediction, radiomics, texture analysis, FDG-PET/CT fusion, head and neck cancer.

Graphical Abstract
Institute NC. Head and Neck Cancers. National Cancer Institute 2017.
Rebecca M, Siegel L, Kimberly D, et al. Cancer Statistics, 2019. Available online at:
Bray F, Ferlay J, Soerjomataram I, Siegel R L, Torre L A, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2018; 68: 394-424.
Kou F, Liu W. Immuntherapy in recurrent or metastatic head and squamous cell carcinoma: current status and future explorations Chin J Cancer Prec Treat 2017; 24: 1102-8.
Wang J, Li Y, Zhao Q, Ma Y, Sun X, Li J. 18F-FDGPET/CT in prognostic evaluation of patients with lung cance Chin J Med Imaging Technol 2019; 35: 1028-32.
Zheng L, Liu G, Zhang W, Zhang X, Ding Z. Prognostic evaluation of 18F-FDG PET/CT in patients with pancreatic head carcinoma. Chin J Med Imaging Technol 2018; 34: 1237-41.
Wang S, Wang T, Zhang T, Tian X. Research progress in application of PET/MRI in diagnosis of head and neck squamous cell carcinoma Journal of Jilin University(Medicine Edition) 2019; 45: 206-10.
Vallières M, Kay-Rivest E, Perrin LJ, et al. Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer. Scientific Reports 2017; 7.
Vallières M, Freeman CR, Skamene SR, El Naqa I. A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremitiesPhysics in medicine and biology. 2015; 60: pp. 5471-96.
Liu H, Guo D, Qiu T. Medical Imaging and Medical Image Processing. Publishing House Of Electronics Insustry 2013.
Al KE. Calculation of SUVbw and SUVbsa are according to the following equations. J Nucl Med 1994; 35: 164-7.
Xie K, Sun H, Lin T, Gao L, Sui J, Ni X. Research progresses in feature extraction of radiomics Chin J Med Imaging Technol 2017; 33: 1792-6.
Hinton GE, Salakhutdinov RR. Reducing the Dimensionality of Data with Neural NetworksScience. 2006; 313: pp. 504-7.
Zhou Z. Machine learning. TSINGHUA UNVIERSITY PRESS 2016.
Bogowicz M, Riesterer O, Stark LS, et al. Comparison of PET and CT radiomics for prediction of local tumor control in head and neck squamous cell carcinomaActa oncologica (Stockholm, Sweden). 2017; 56: pp. 1531-6.
Chou Y, Qiu T, Zhong M. Classification and Recognition of P300 Event Related Potential Based on Convolutional Neural Network Chinese Journal of Biomedical Engineering 2018; 37: 657-64.
Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 2012; 48(4): 441-6.
[] [PMID: 22257792]
Kumar V, Gu Y, Basu S, et al. Radiomics: the process and the challenges. Magn Reson Imaging 2012; 30(9): 1234-48.
[] [PMID: 22898692]
Liu Z, Liang Z. Promoting translational research of radiomics Chin J Med Imaging Technol 2017; 33: 1765-7.
Min M, Zhu M, Zheng H. Recent advances in early diagnosis of head and neck cancer in precision medicine era J Clin Otorhinolaryngol Head Neck Surg(China) 2017; 31: 1770-4.
Park S, Lee S M, Do K, et al. Deep Learning Algorithm for Reducing CT Slice Thickness: Effect on Reproducibility of Radiomic Features in Lung Cancer Korean Journal of Radiology 2019; 20: 1431-40.
Li Q, Qi L, Feng Q X, et al. Machine Learning-Based Computational Models Derived From Large-Scale Radiographic-Radiomic Images Can Help Predict Adverse Histopathological Status of Gastric Cancer Clin Transl Gastroenterol 2019; 10: e00079.
Diamant A, Chatterjee A, Vallières M, Shenouda G, Seuntjens J. Deep learning in head & neck cancer outcome prediction. Scientific Reports 2019; 9.
Moan JM, Amdal CD, Malinen E, Svestad JG, Bogsrud TV, Dale E. The prognostic role of 18F-fluorodeoxyglucose PET in head and neck cancer depends on HPV status. Radiother Oncol 2019; 140: 54-61.
[] [PMID: 31177043]
Kolossváry M, Park J, Bang J, et al. Identification of invasive and radionuclide imaging markers of coronary plaque vulnerability using radiomic analysis of coronary computed tomography angiographyEuropean Heart Journal - Cardiovascular Imaging. 2019; 20: pp. 1250-8.
Brown P J, Zhong J, Frood R, et al. Prediction of outcome in anal squamous cell carcinoma using radiomic feature analysis of pre-treatment FDG PET-CT Eur J Nucl Med Mol Imaging 2019; 46: 2790-9.
Mayerhoefer M E, Riedl C C, Kumar A, et al. Radiomic features of glucose metabolism enable prediction of outcome in mantle cell lymphoma European Journal of Nuclear Medicine and Molecular Imaging 2019; 46: 2760-9.
Hinton GE, Salakhutdinov RR. Reducing the Dimensionality of Data with Neural Networks. Science. 2006; 313: pp. 504-7.
Vallières M, Kay-Rivest E, Perrin L J, et al. Data from Head- Neck-PET-CT:The Cancer Imaging Archive 2017.

Rights & Permissions Print Export Cite as
© 2022 Bentham Science Publishers | Privacy Policy