Pan Mulan, Lu Lu, Mu Xingyu, Jin Guanqiao
Department of Radiology, Guangxi Medical University Cancer Hospital, Guangxi Clinical Research Center for Imaging Medicine, Guangxi Clinical Key Specialty (Medical Imaging), Key Discipline Development Program (Medical Imaging), Affiliated Cancer Hospital of Guangxi Medical University, Nanning, China.
Department of Nuclear Medicine, Guangxi Academy of Medical Sciences, People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China.
Front Oncol. 2025 May 14;15:1539574. doi: 10.3389/fonc.2025.1539574. eCollection 2025.
This study aims to predict the early efficacy of induction chemotherapy (ICT) in patients with locally advanced nasopharyngeal carcinoma (LA-NPC) through habitat subregion analysis and multimodal MRI radiomics techniques.
The study employed a retrospective design and included LA-NPC patients who received ICT treatment between 2015 and 2019. The K-means clustering algorithm was utilized to segment the tumor into five distinct habitat subregions based on imaging features. A total of 2,153 radiomic features, including geometric shape, intensity, and texture features, were extracted. Feature selection was conducted using the maximum relevance minimum redundancy (mRMR) method and the least absolute shrinkage and selection operator (LASSO) technique. Eleven machine learning algorithms were employed to develop radiomics models based on the CE-T1WI and T2-FS sequences, respectively. These models were evaluated using various predictive performance metrics, including area under the curve (AUC), sensitivity, and specificity. Model selection was based on comprehensive cross-validation performance and AUC values.
The study population comprised 76.63% males and 23.37% females, with a mean age of 42.60 ± 10.21 years. All patients had stage III to IVa nasopharyngeal carcinoma, and the majority (92.39%) had non-keratinizing squamous cell carcinoma. Habitat subregion analysis revealed that the volume features of a specific subregion (Subregion 2) were significantly associated with patient response to ICT ( = 0.032). The RF model built using radiomic features from Subregion 2 demonstrated the best performance on the CE-T1WI sequence, with an AUC of 0.921 in the training set and 0.819 in the testing set. On the T2-FS sequence, the Random Forest (RF) model also exhibited high diagnostic performance, with an AUC of 0.933 in the training set and 0.829 in the testing set. These results suggest that the RF model provides stable and reliable predictive performance across different MRI sequences.
Habitat subregion analysis using multimodal MRI radiomics offers an effective approach for the early identification of LA-NPC patients with poor responses to induction chemotherapy. This method holds promise for supporting clinical treatment decisions and achieving personalized medicine.
本研究旨在通过瘤区亚区域分析和多模态MRI影像组学技术预测局部晚期鼻咽癌（LA-NPC）患者诱导化疗（ICT）的早期疗效。
本研究采用回顾性设计，纳入2015年至2019年间接受ICT治疗的LA-NPC患者。利用K均值聚类算法根据影像特征将肿瘤分为五个不同的瘤区亚区域。共提取了2153个影像组学特征，包括几何形状、强度和纹理特征。使用最大相关最小冗余（mRMR）方法和最小绝对收缩和选择算子（LASSO）技术进行特征选择。分别基于CE-T1WI和T2-FS序列，采用11种机器学习算法构建影像组学模型。使用包括曲线下面积（AUC）、敏感性和特异性在内的各种预测性能指标对这些模型进行评估。模型选择基于综合交叉验证性能和AUC值。
研究人群中男性占76.63%，女性占23.37%，平均年龄为42.60±10.21岁。所有患者均为III至IVa期鼻咽癌，大多数（92.39%）为非角化鳞状细胞癌。瘤区亚区域分析显示，特定亚区域（亚区域2）的体积特征与患者对ICT的反应显著相关（P = 0.032）。使用亚区域2的影像组学特征构建的随机森林（RF）模型在CE-T1WI序列上表现最佳，训练集的AUC为0.921，测试集的AUC为0.819。在T2-FS序列上，随机森林（RF）模型也表现出较高的诊断性能，训练集的AUC为0.933，测试集的AUC为0.829。这些结果表明，RF模型在不同的MRI序列上提供了稳定可靠的预测性能。
使用多模态MRI影像组学进行瘤区亚区域分析为早期识别对诱导化疗反应不佳的LA-NPC患者提供了一种有效方法。该方法有望支持临床治疗决策并实现个性化医疗。
