Wang Yiren, Li Yunfei, Chen Shouying, Wen Zhongjian, Hu Yiheng, Zhang Huaiwen, Zhou Ping, Pang Haowen
School of Nursing, Southwest Medical University, Luzhou, China.
Wound Healing Basic Research and Clinical Application Key Laboratory of Luzhou, Luzhou, China.
Med Phys. 2025 May;52(5):3243-3257. doi: 10.1002/mp.17639. Epub 2025 Jan 27.
Kidney tumors, common in the urinary system, have widely varying survival rates post-surgery. Current prognostic methods rely on invasive biopsies, highlighting the need for non-invasive, accurate prediction models to assist in clinical decision-making.
This study aimed to construct a K-means clustering algorithm enhanced by Transformer-based feature transformation to predict the overall survival rate of patients after kidney tumor resection and provide an interpretability analysis of the model to assist in clinical decision-making.
This study was based on a publicly available C4KC-KiTS-2019 dataset from the TCIA database, including preoperative computed tomography (CT) images and survival time data of 210 patients. Initially, the radiomics features of the kidney tumor area were extracted using the 3D slicer software. Feature selection was then conducted using ICC, mRMR algorithms, and LASSO regression to calculate radiomics scores. Subsequently, the selected features were input into a pre-trained Transformer model for feature transformation to obtain a higher-dimensional feature set. Then, K-means clustering was performed using this feature set, and the model was evaluated using receiver operating characteristic (ROC) and Kaplan-Meier curves. Finally, the SHAP interpretability algorithm was used for the feature importance analysis of the K-means clustering results.
Eleven important features were selected from 851 radiomics features. The K-means clustering model after Transformer feature transformation showed AUCs of 0.889, 0.841, and 0.926 for predicting 1-, 3-, and 5-year overall survival rates, respectively, thereby outperforming both the K-means model with original feature inputs and the radiomics score method. A clustering analysis revealed survival prognosis differences among different patient groups, and a SHAP analysis provided insights into the features that had the most significant impacts on the model predictions.
The K-means clustering algorithm enhanced by the Transformer feature transformation proposed in this study demonstrates promising accuracy and interpretability in predicting the overall survival rate after kidney tumor resection. This method provides a valuable tool for clinical decision-making and contributes to improved management and treatment strategies for patients with kidney tumors.
肾肿瘤在泌尿系统中较为常见，术后生存率差异很大。目前的预后方法依赖于侵入性活检，这凸显了需要非侵入性、准确的预测模型来辅助临床决策。
本研究旨在构建一种基于Transformer特征变换增强的K均值聚类算法，以预测肾肿瘤切除术后患者的总生存率，并对模型进行可解释性分析以辅助临床决策。
本研究基于TCIA数据库中公开的C4KC-KiTS-2019数据集，包括210例患者的术前计算机断层扫描（CT）图像和生存时间数据。最初，使用3D Slicer软件提取肾肿瘤区域的放射组学特征。然后使用ICC、mRMR算法和LASSO回归进行特征选择，以计算放射组学评分。随后，将所选特征输入到预训练的Transformer模型中进行特征变换，以获得更高维的特征集。然后，使用该特征集进行K均值聚类，并使用受试者操作特征（ROC）曲线和Kaplan-Meier曲线对模型进行评估。最后，使用SHAP可解释性算法对K均值聚类结果进行特征重要性分析。
从851个放射组学特征中选择了11个重要特征。经过Transformer特征变换后的K均值聚类模型预测1年、3年和5年总生存率的AUC分别为0.889、0.841和0.926，从而优于原始特征输入的K均值模型和放射组学评分方法。聚类分析揭示了不同患者组之间的生存预后差异，SHAP分析提供了对模型预测影响最大的特征的见解。
本研究提出的基于Transformer特征变换增强的K均值聚类算法在预测肾肿瘤切除术后的总生存率方面显示出有前景的准确性和可解释性。该方法为临床决策提供了有价值的工具，并有助于改善肾肿瘤患者的管理和治疗策略。
