Department of Radiology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Qingdao, 266035, China.
Department of Radiology, Qilu Hospital of Shandong University, Jinan, China.
Abdom Radiol (NY). 2024 Jul;49(7):2311-2324. doi: 10.1007/s00261-024-04418-1. Epub 2024 Jun 15.
To develop and validate a predictive combined model for metastasis in patients with clear cell renal cell carcinoma (ccRCC) by integrating multimodal data.
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.
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.
The combined model was superior to the clinical and radiomics models in predicting LNM in ccRCC patients.
通过整合多模态数据，开发并验证用于透明细胞肾细胞癌（ccRCC）患者转移预测的联合模型。
本回顾性研究纳入了来自三个不同地区三级医院经病理证实的 ccRCC 患者的临床和影像学（CT 和超声）数据。我们构建了三个模型，包括临床模型、影像组学模型和联合模型。通过比较模型的判别能力和临床实用性来评估模型的性能。评估指标包括受试者工作特征曲线（ROC）下面积（AUC）值、准确率、敏感度、特异度、阴性预测值、阳性预测值和决策曲线分析（DCA）曲线。
共纳入 251 例患者，其中山东大学齐鲁医院（济南）的 166 例患者分为训练队列，50 例患者发生转移；山东大学齐鲁医院（青岛）的 37 例患者用于内部验证，15 例患者发生转移；常州第二人民医院的 48 例患者用于外部验证，13 例患者发生转移。在训练集中，联合模型在预测淋巴结转移（LNM）方面表现最佳（AUC：0.924），而临床模型和影像组学模型的 AUC 分别为 0.845 和 0.870。在内部验证中，联合模型在预测 LNM 方面表现最佳（AUC：0.877），而临床模型和影像组学模型的 AUC 分别为 0.726 和 0.836。在外部验证中，联合模型在预测 LNM 方面表现最佳（AUC：0.849），而临床模型和影像组学模型的 AUC 分别为 0.708 和 0.804。DCA 曲线表明，与临床模型或影像组学模型相比，联合模型在预测 ccRCC 患者 LNM 风险方面具有显著的预测概率。
与临床模型和影像组学模型相比，联合模型在预测 ccRCC 患者 LNM 方面表现更优。
