Shi Jinyu, Wang Lian, Zhou Min, Ge Shushan, Zhang Bin, Han Jiangqin, Li Jihui, Deng Shengming
Department of Nuclear Medicine, The First Affiliated Hospital of Soochow University, Suzhou, China.
Department of Oncology, Xuyi People's Hospital, Huaian, China.
Front Oncol. 2025 May 2;15:1486654. doi: 10.3389/fonc.2025.1486654. eCollection 2025.
This study sought to develop an advanced composite model to enhance the prognostic accuracy for cervical cancer patients undergoing concurrent chemoradiotherapy (CCRT). The model integrated imaging features from [F]FDG PET/CT scans with inflammatory markers using a novel unsupervised two-way clustering approach.
In this retrospective study, 154 patients diagnosed with primary cervical cancer and treated with CCRT were evaluated using [F]FDG PET/CT scans. A total of 1,702 radiomic features were extracted from the imaging data. These features underwent rigorous selection based on reproducibility and non-redundancy. The unsupervised two-way clustering method was then employed to simultaneously stratify patients and reduce the dimensionality of features, resulting in the generation of meta-features that were subsequently used to predict overall survival.
Kaplan-Meier survival analysis demonstrated that the two-way clustering method successfully stratified patients into distinct risk groups with significant survival differences (P<0.001), outperforming traditional K-means clustering. Predictive models constructed using meta-features derived from two-way clustering showed superior performance compared to those using principal component analysis (PCA), particularly when more than four features were included. The highest C-index values for the COX, COX_Lasso, and RSF models were observed with nine meta-features, yielding results of 0.691 ± 0.026, 0.634 ± 0.018, and 0.684 ± 0.020, respectively. In contrast, models based solely on clinical variables exhibited lower predictive performance, with C-index values of 0.645 ± 0.041, 0.567 ± 0.016, and 0.561 ± 0.033. The combination of clinical data, inflammatory markers, and radiomic features achieved the highest predictive accuracy, with a mean AUC of 0.88 ± 0.07.
Integrating radiomic data with inflammatory markers using unsupervised two-way clustering offered a robust approach for predicting survival outcomes in cervical cancer patients. This methodology presented a promising avenue for personalized patient management, potentially leading to more informed treatment decisions and improved outcomes.
本研究旨在开发一种先进的复合模型，以提高接受同步放化疗（CCRT）的宫颈癌患者的预后准确性。该模型使用一种新颖的无监督双向聚类方法，将[F]FDG PET/CT扫描的影像特征与炎症标志物相结合。
在这项回顾性研究中，对154例诊断为原发性宫颈癌并接受CCRT治疗的患者进行了[F]FDG PET/CT扫描评估。从影像数据中提取了总共1702个影像组学特征。这些特征基于可重复性和非冗余性进行了严格筛选。然后采用无监督双向聚类方法对患者进行分层并降低特征维度，从而生成元特征，随后用于预测总生存期。
Kaplan-Meier生存分析表明，双向聚类方法成功地将患者分为不同的风险组，生存差异显著（P<0.001），优于传统的K均值聚类。使用双向聚类衍生的元特征构建的预测模型比使用主成分分析（PCA）的模型表现更优，尤其是当包含四个以上特征时。COX、COX_Lasso和RSF模型在九个元特征时观察到最高的C指数值，分别为0.691±0.026、0.634±0.018和0.684±0.020。相比之下，仅基于临床变量的模型预测性能较低，C指数值分别为0.645±0.041、0.567±0.016和0.561±0.033。临床数据、炎症标志物和影像组学特征的组合实现了最高的预测准确性，平均AUC为0.88±0.07。
使用无监督双向聚类将影像组学数据与炎症标志物相结合，为预测宫颈癌患者的生存结局提供了一种强大的方法。这种方法为个性化患者管理提供了一条有前景的途径，可能导致更明智的治疗决策和更好的结局。
