Abstract：
OBJECTIVE: To develop and validate a predictive combined model for metastasis in patients with clear cell renal cell carcinoma (ccRCC) by integrating multimodal data.
METHODS: In this retrospective study, the clinical and imaging data (CT and ultrasound) of patients with ccRCC confirmed by pathology from three tertiary hospitals in different regions were collected from January 2013 to January 2023. We developed three models, including a clinical model, a radiomics model, and a combined model. The performance of the model was determined based on its discriminative power and clinical utility. The evaluation indicators included area under the receiver operating characteristic curve (AUC) value, accuracy, sensitivity, specificity, negative predictive value, positive predictive value and decision curve analysis (DCA) curve.
RESULTS: A total of 251 patients were evaluated. Patients (n = 166) from Shandong University Qilu Hospital (Jinan) were divided into the training cohort, of which 50 patients developed metastases; patients (n = 37) from Shandong University Qilu Hospital (Qingdao) were used as internal testing, of which 15 patients developed metastases; patients (n = 48) from Changzhou Second People\'s Hospital were used as external testing, of which 13 patients developed metastases. In the training set, the combined model showed the highest performance (AUC, 0.924) in predicting lymph node metastasis (LNM), while the clinical and radiomics models both had AUCs of 0.845 and 0.870, respectively. In the internal testing, the combined model had the highest performance (AUC, 0.877) for predicting LNM, while the AUCs of the clinical and radiomics models were 0.726 and 0.836, respectively. In the external testing, the combined model had the highest performance (AUC, 0.849) for predicting LNM, while the AUCs of the clinical and radiomics models were 0.708 and 0.804, respectively. The DCA curve showed that the combined model had a significant prediction probability in predicting the risk of LNM in ccRCC patients compared with the clinical model or the radiomics model.
CONCLUSIONS: The combined model was superior to the clinical and radiomics models in predicting LNM in ccRCC patients.
METHODS: In this retrospective study, the clinical and imaging data (CT and ultrasound) of patients with ccRCC confirmed by pathology from three tertiary hospitals in different regions were collected from January 2013 to January 2023. We developed three models, including a clinical model, a radiomics model, and a combined model. The performance of the model was determined based on its discriminative power and clinical utility. The evaluation indicators included area under the receiver operating characteristic curve (AUC) value, accuracy, sensitivity, specificity, negative predictive value, positive predictive value and decision curve analysis (DCA) curve.
RESULTS: A total of 251 patients were evaluated. Patients (n = 166) from Shandong University Qilu Hospital (Jinan) were divided into the training cohort, of which 50 patients developed metastases; patients (n = 37) from Shandong University Qilu Hospital (Qingdao) were used as internal testing, of which 15 patients developed metastases; patients (n = 48) from Changzhou Second People\'s Hospital were used as external testing, of which 13 patients developed metastases. In the training set, the combined model showed the highest performance (AUC, 0.924) in predicting lymph node metastasis (LNM), while the clinical and radiomics models both had AUCs of 0.845 and 0.870, respectively. In the internal testing, the combined model had the highest performance (AUC, 0.877) for predicting LNM, while the AUCs of the clinical and radiomics models were 0.726 and 0.836, respectively. In the external testing, the combined model had the highest performance (AUC, 0.849) for predicting LNM, while the AUCs of the clinical and radiomics models were 0.708 and 0.804, respectively. The DCA curve showed that the combined model had a significant prediction probability in predicting the risk of LNM in ccRCC patients compared with the clinical model or the radiomics model.
CONCLUSIONS: The combined model was superior to the clinical and radiomics models in predicting LNM in ccRCC patients.
摘要：
目的：通过整合多模态数据，开发和验证透明细胞肾细胞癌（ccRCC）患者转移的预测性组合模型。
方法：在这项回顾性研究中，收集2013年1月至2023年1月不同地区三家三级医院经病理证实的ccRCC患者的临床和影像学资料(CT和超声).我们开发了三种模型，包括临床模型，一个影像组学模型，和组合模型。模型的性能是根据其判别力和临床实用性确定的。评价指标包括受试者工作特征曲线下面积(AUC)值,准确度,灵敏度,特异性，负预测值，阳性预测值和决策曲线分析(DCA)曲线。
结果：共评估了251例患者。将山东大学齐鲁医院（济南）的患者（n=166）分为培训队列，其中50例患者发生转移；山东大学齐鲁医院（青岛）的患者（n=37）作为内部检测，其中15例患者发生转移；常州市第二人民医院的患者（n=48）作为外部检测，其中13例发生转移。在训练集中，组合模型显示出最高的性能(AUC，0.924)在预测淋巴结转移(LNM)中，而临床和影像组学模型的AUC分别为0.845和0.870.在内部测试中，组合模型具有最高的性能(AUC，0.877)用于预测LNM，而临床和影像组学模型的AUC分别为0.726和0.836。在外部测试中，组合模型具有最高的性能(AUC，0.849)用于预测LNM，而临床和影像组学模型的AUC分别为0.708和0.804。DCA曲线显示，与临床模型或影像组学模型相比，组合模型在预测ccRCC患者LNM风险方面具有显著的预测概率。
结论：联合模型在预测ccRCC患者的LNM方面优于临床和影像组学模型。
方法：在这项回顾性研究中，收集2013年1月至2023年1月不同地区三家三级医院经病理证实的ccRCC患者的临床和影像学资料(CT和超声).我们开发了三种模型，包括临床模型，一个影像组学模型，和组合模型。模型的性能是根据其判别力和临床实用性确定的。评价指标包括受试者工作特征曲线下面积(AUC)值,准确度,灵敏度,特异性，负预测值，阳性预测值和决策曲线分析(DCA)曲线。
结果：共评估了251例患者。将山东大学齐鲁医院（济南）的患者（n=166）分为培训队列，其中50例患者发生转移；山东大学齐鲁医院（青岛）的患者（n=37）作为内部检测，其中15例患者发生转移；常州市第二人民医院的患者（n=48）作为外部检测，其中13例发生转移。在训练集中，组合模型显示出最高的性能(AUC，0.924)在预测淋巴结转移(LNM)中，而临床和影像组学模型的AUC分别为0.845和0.870.在内部测试中，组合模型具有最高的性能(AUC，0.877)用于预测LNM，而临床和影像组学模型的AUC分别为0.726和0.836。在外部测试中，组合模型具有最高的性能(AUC，0.849)用于预测LNM，而临床和影像组学模型的AUC分别为0.708和0.804。DCA曲线显示，与临床模型或影像组学模型相比，组合模型在预测ccRCC患者LNM风险方面具有显著的预测概率。
结论：联合模型在预测ccRCC患者的LNM方面优于临床和影像组学模型。
