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

无线电技术 医学 头颈部癌 头颈部 放射科 人工智能 放射治疗 核医学 计算机科学 外科
作者
Cong Hu,Peng Wang,Tian Zhou,Martin Vallières,Chuanpei Xu,Aijun Zhu,Benxin Zhang
出处
期刊:Current Medical Imaging Reviews [Bentham Science]
卷期号:17 (3): 374-383 被引量:1
标识
DOI:10.2174/1573405616666200712181135
摘要

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.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.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.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.
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