Yu Xinxin, Gao Lin, Zhang Shuai, Sun Cong, Zhang Juntao, Kang Bing, Wang Ximing
Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
School of Medicine, Shandong University, Jinan, China.
Front Oncol. 2023 Jan 4;12:1016583. doi: 10.3389/fonc.2022.1016583. eCollection 2022.
Early identification of synchronous distant metastasis (SDM) in patients with clear cell Renal cell carcinoma (ccRCC) can certify the reasonable diagnostic examinations.
This retrospective study recruited 463 ccRCC patients who were divided into two cohorts (training and internal validation) at a 7:3 ratio. Besides, 115 patients from other hospital were assigned external validation cohort. A radiomics signature was developed based on features by means of the least absolute shrinkage and selection operator method. Demographics, laboratory variables and CT findings were combined to develop clinical factors model. Integrating radiomics signature and clinical factors model, a radiomics nomogram was developed.
Ten features were used to build radiomics signature, which yielded an area under the curve (AUC) 0.882 in the external validation cohort. By incorporating the clinical independent predictors, the clinical model was developed with AUC of 0.920 in the external validation cohort. Radiomics nomogram (external validation, 0.925) had better performance than clinical factors model or radiomics signature. Decision curve analysis demonstrated the superiority of the radiomics nomogram in terms of clinical usefulness.
The CT-based nomogram could help in predicting SDM status in patients with ccRCC, which might provide assistance for clinicians in making diagnostic examinations.
早期识别透明细胞肾细胞癌（ccRCC）患者的同步远处转移（SDM）有助于确定合理的诊断检查。
本回顾性研究纳入了463例ccRCC患者，按7:3的比例分为两个队列（训练队列和内部验证队列）。此外，将来自其他医院的115例患者分配到外部验证队列。基于特征，采用最小绝对收缩和选择算子方法构建了一个影像组学特征。结合人口统计学、实验室变量和CT表现，建立了临床因素模型。将影像组学特征与临床因素模型相结合，构建了影像组学列线图。
使用10个特征构建影像组学特征，在外部验证队列中其曲线下面积（AUC）为0.882。通过纳入临床独立预测因子，建立了临床模型，在外部验证队列中的AUC为0.920。影像组学列线图（外部验证，0.925）的表现优于临床因素模型或影像组学特征。决策曲线分析表明影像组学列线图在临床实用性方面具有优势。
基于CT的列线图有助于预测ccRCC患者的SDM状态，可为临床医生进行诊断检查提供帮助。
