He Wan-Bin, Zhou Chuan, Yang Zhi-Jun, Zhang Yun-Feng, Zhang Wen-Bo, He Han, Wang Jia, Zhou Feng-Hai
The First Clinical Medical College of Lanzhou University, Lanzhou, 73000, China.
The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou, 730000, China.
Discov Oncol. 2025 Jan 25;16(1):86. doi: 10.1007/s12672-025-01806-x.
The objective of this research was to devise and authenticate a predictive model that employs CT radiomics and deep learning methodologies for the accurate prediction of synchronous distant metastasis (SDM) in clear cell renal cell carcinoma (ccRCC).
A total of 143 ccRCC patients were included in the training cohort, and 62 ccRCC patients were included in the validation cohort. The CT images from all patients were normalized, and the tumor regions were manually segmented via ITK-SNAP software. Radiomic features were extracted via the FAE toolkit. The least absolute shrinkage and selection operator (LASSO) algorithm was employed to select features and build various machine learning models. Additionally, the largest cross-section of the tumor was cropped to train the deep learning model. Multiple deep learning models were trained to predict SDM in ccRCC patients. The results of the best machine learning model were then fused with those of the deep learning model to create a combined model.
Of the 944 radiomic features identified, 15 were closely associated with SDM. With these 15 features, the support vector machine (SVM) model emerged as the most effective, demonstrating areas under the curve (AUC) of 0.860 and 0.813 in the training and validation cohort, respectively. Among the deep learning models, ResNet101 performed optimally, achieving AUC of 0.815 and 0.743 in the training and validation cohort, respectively. The combined model yielded an AUC of 0.863. Decision curve analysis suggested that the combined model offers superior clinical applicability.
The model integrates radiomics and deep learning, showing significant potential in predicting SDM in ccRCC patients. It holds promise for supporting clinical decision-making, reducing missed diagnoses of SDM, and guiding patients in further enhancing their systemic examinations.
本研究的目的是设计并验证一种预测模型，该模型采用CT影像组学和深度学习方法来准确预测透明细胞肾细胞癌（ccRCC）中的同步远处转移（SDM）。
训练队列纳入了143例ccRCC患者，验证队列纳入了62例ccRCC患者。对所有患者的CT图像进行归一化处理，并通过ITK-SNAP软件手动分割肿瘤区域。通过FAE工具包提取影像组学特征。采用最小绝对收缩和选择算子（LASSO）算法选择特征并构建各种机器学习模型。此外，裁剪肿瘤的最大横截面来训练深度学习模型。训练多个深度学习模型以预测ccRCC患者的SDM。然后将最佳机器学习模型的结果与深度学习模型的结果融合，创建一个联合模型。
在识别出的944个影像组学特征中，有15个与SDM密切相关。利用这15个特征，支持向量机（SVM）模型成为最有效的模型，在训练队列和验证队列中的曲线下面积（AUC）分别为0.860和0.813。在深度学习模型中，ResNet101表现最佳，在训练队列和验证队列中的AUC分别为0.815和0.743。联合模型的AUC为0.863。决策曲线分析表明联合模型具有更好的临床适用性。
该模型整合了影像组学和深度学习，在预测ccRCC患者的SDM方面显示出巨大潜力。它有望支持临床决策，减少SDM的漏诊，并指导患者进一步加强全身检查。
