Yang Lu, Yu Longwu, Shi Guangzi, Yang Lingjie, Wang Yu, Han Riyu, Huang Fengqiong, Qian Yinfeng, Duan Xiaohui
Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou 510120, Guangdong, China.
Department of Radiology, The First Affiliated Hospital of Anhui Medical University, No. 218 Jixi Road, Hefei 230022, Anhui Province, China.
Magn Reson Imaging. 2025 Feb;116:110276. doi: 10.1016/j.mri.2024.110276. Epub 2024 Nov 19.
This study aimed to investigate the potential of radiomic features derived from dynamic contrast-enhanced MRI (DCE-MRI) in predicting Ki-67 and p16 status in head and neck squamous cell carcinoma (HNSCC).
A cohort of 124 HNSCC patients who underwent pre-surgery DCE-MRI were included and divided into training and test set (7:3), further subgroup analysis was performed for 104 cases with oral squamous cell carcinoma (OSCC). Radiomics features were extracted from DCE images. The least absolute shrinkage and selection operator (LASSO) was used for radiomics features selection, and receiver operating characteristics analysis for predictive performance assessment. The nomogram's performance was evaluated using decision curve analysis (DCA).
Ten DCE-MRI features were identified to build the predictive model of HNSCC, demonstrating excellent predictive value for Ki-67 status in both the training set (AUC of 0.943) and test set (AUC of 0.801). The nomograms based on the predictive model showed good fit in the calibration curves (p > 0.05), and DCA indicated its high clinical usefulness. In subgroup analysis of OSCC, fourteen features were selected to build the predictive model for Ki-67 status with an AUC of 0.960 in training set and 0.817 in test set. No features could be included to establish a model to predict p16 status.
The radiomics model utilizing DCE-MRI features could effectively predict Ki-67 status in HNSCC patients, offering potential for noninvasive preoperative prediction of Ki-67 status.
本研究旨在探讨动态对比增强磁共振成像（DCE-MRI）衍生的放射组学特征在预测头颈部鳞状细胞癌（HNSCC）中Ki-67和p16状态方面的潜力。
纳入124例接受术前DCE-MRI检查的HNSCC患者，分为训练集和测试集（7:3），对104例口腔鳞状细胞癌（OSCC）患者进行进一步亚组分析。从DCE图像中提取放射组学特征。采用最小绝对收缩和选择算子（LASSO）进行放射组学特征选择，并通过受试者工作特征分析评估预测性能。使用决策曲线分析（DCA）评估列线图的性能。
确定了10个DCE-MRI特征来构建HNSCC的预测模型，该模型在训练集（AUC为0.943）和测试集（AUC为0.801）中对Ki-67状态均显示出优异的预测价值。基于预测模型的列线图在校准曲线中显示出良好的拟合度（p>0.05），DCA表明其具有较高的临床实用性。在OSCC的亚组分析中，选择了14个特征来构建Ki-67状态的预测模型，训练集AUC为0.960，测试集AUC为0.817。无法纳入任何特征来建立预测p16状态的模型。
利用DCE-MRI特征的放射组学模型可以有效预测HNSCC患者的Ki-67状态，为Ki-67状态的术前无创预测提供了潜力。
