目的:利用临床数据评估vViT模型预测术后肾功能下降的有效性,医学图像,和图像衍生特征;并确定影响这一预测的最主要因素。
方法:我们开发了两种模型,eGFR10和eGFR20,分别识别术后eGFR降低10以上和20以上的患者,在肾细胞癌患者中。eGFR10模型在75名患者上进行了训练,并在27名患者上进行了测试,而eGFR20模型在77名患者上进行了训练,并在24名患者上进行了测试。vViT模型输入包括类标记,患者特征(年龄,性别,BMI),合并症(外周血管疾病,糖尿病,肝病),习惯(吸烟,alcohol),手术细节(缺血时间,失血,手术的类型和程序,方法,手术时间),影像组学,肿瘤和肾脏成像。我们使用排列特征重要性来评估每个扇区的贡献。将vViT的性能与CNN模型进行了比较,包括VGG16、ResNet50和DenseNet121,使用McNemar和DeLong测试。
结果:eGFR10模型的准确度为0.741,AUC-ROC为0.692,而eGFR20模型的准确度为0.792,AUC-ROC为0.812。在这两个模型中,外科和影像组学部门最具影响力。与VGG16和ResNet50相比,vViT具有更高的准确性和AUC-ROC,而与DenseNet121相比,AUC-ROC更高(p<0.05)。具体来说,对于eGFR10模型,vViT与VGG16(p=1.0)和ResNet50(p=0.7)相比,AUC-ROC无统计学差异,但与DenseNet121(p=0.87)相比,AUC-ROC有统计学差异.对于eGFR20型号,与VGG16相比,vViT没有统计学上不同的AUC-ROC(p=0.72),ResNet50(p=0.88),和DenseNet121(p=0.64)。
结论:vViT模型,一种基于变压器的多模态数据方法,显示了肾细胞癌患者术前基于CT的eGFR状态预测的前景。
OBJECTIVE: To assess the effectiveness of the vViT model for predicting postoperative renal function decline by leveraging clinical data, medical images, and image-derived features; and to identify the most dominant factor influencing this prediction.
METHODS: We developed two models, eGFR10 and eGFR20, to identify patients with a postoperative reduction in eGFR of more than 10 and more than 20, respectively, among renal cell carcinoma patients. The eGFR10 model was trained on 75 patients and tested on 27, while the eGFR20 model was trained on 77 patients and tested on 24. The vViT model inputs included class token, patient characteristics (age, sex, BMI), comorbidities (peripheral vascular disease, diabetes, liver disease), habits (smoking, alcohol), surgical details (ischemia time, blood loss, type and procedure of surgery, approach, operative time), radiomics, and tumor and kidney imaging. We used permutation feature importance to evaluate each sector\'s contribution. The performance of vViT was compared with CNN models, including VGG16, ResNet50, and DenseNet121, using McNemar and DeLong tests.
RESULTS: The eGFR10 model achieved an accuracy of 0.741 and an AUC-ROC of 0.692, while the eGFR20 model attained an accuracy of 0.792 and an AUC-ROC of 0.812. The surgical and radiomics sectors were the most influential in both models. The vViT had higher accuracy and AUC-ROC than VGG16 and ResNet50, and higher AUC-ROC than DenseNet121 (p < 0.05). Specifically, the vViT did not have a statistically different AUC-ROC compared to VGG16 (p = 1.0) and ResNet50 (p = 0.7) but had a statistically different AUC-ROC compared to DenseNet121 (p = 0.87) for the eGFR10 model. For the eGFR20 model, the vViT did not have a statistically different AUC-ROC compared to VGG16 (p = 0.72), ResNet50 (p = 0.88), and DenseNet121 (p = 0.64).
CONCLUSIONS: The vViT model, a transformer-based approach for multimodal data, shows promise for preoperative CT-based prediction of eGFR status in patients with renal cell carcinoma.