School of Electronic Engineering and Automation, Guilin University of Electronic Technology, , Guilin, China.
School of Electronic Information and Automation, Guilin University of Aerospace Technology, Guilin, China.
Curr Med Imaging. 2021;17(3):374-383. doi: 10.2174/1573405616666200712181135.
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.
CT 和 PET 放射组学均被认为是头颈部癌症的潜在预后生物标志物。本研究旨在探讨融合预处理功能成像（18F-FDG PET/CT）放射组学对头颈部癌症局部复发建模的价值。
首先，将 298 例患者分为训练集（n=192）和验证集（n=106）。其次，基于小波变换将 PET 和 CT 融合。然后，从 PETCT 融合的 3D 肿瘤区域提取放射组学特征。使用训练集选择特征减少并预测局部复发，并构建结合放射组学和临床变量的随机森林预测模型。最后，使用 ROC 曲线和 KM 分析评估模型在验证集上的预测效率。
从融合的 PET/CT 中提取了两个放射组学特征和三个临床参数，构建了放射组学模型。验证集中的 AUC 值为 0.70，优于未融合组的 0.69。准确率为 0.66 （0.67 至 0.69）并非最高值。一致性指数 CI 为 0.70（从 0.67 到 0.70）或 p 值为 0.025（从 0.03 到 0.025），在所有四个模型中均获得最佳结果。
基于 PETCT 融合的放射组学模型在预测局部复发方面优于基于 PET 或 CT 的模型，纳入临床参数可能会导致更准确的预测，这对头颈癌个体化、精准治疗方案的制定具有一定的指导意义。
