Li Xiaoli, Ma Qianli, Tao Cheng, Liu Jinling, Nie Pei, Dong Cheng
Department of Radiology, the Affiliated Hospital of Qingdao University, Qingdao, China.
Department of Radiology, Qingdao Municipal Hospital, Qingdao, China.
Abdom Radiol (NY). 2021 Nov;46(11):5240-5249. doi: 10.1007/s00261-021-03213-6. Epub 2021 Jul 15.
Renal oncocytoma (RO) is the most commonly resected benign renal tumor because of misdiagnosis as renal cell carcinoma. This misdiagnosis is generally owing to overlapping imaging features. This study describes the building of a radiomics nomogram based on clinical data and radiomics signature for the preoperative differentiation of RO from clear cell renal cell carcinoma (ccRCC) on tri-phasic contrast-enhanced CT.
A total of 122 patients (85 in training set and 37 in external validation set) with ROs (n = 46) or ccRCCs (n = 76) were enrolled. Patient characteristics and tri-phasic contrast-enhanced CT imaging features were evaluated to build a clinical factors model. A radiomics signature was constructed by extracting radiomics features from tri-phasic contrast-enhanced CT images and a radiomics score (Rad-score) was calculated. A radiomics nomogram was then built by incorporating the Rad-score and significant clinical factors according to a multivariate logistic regression analysis. The diagnostic performance of the above three models was evaluated in training and validation sets.
Central stellate area and perirenal fascia thickening were selected to build the clinical factors model. Eleven radiomics features were combined to construct the radiomics signature. The AUCs of the radiomics nomogram, which was based on the selected clinical factors and Rad-score, were 0.960 and 0.898 in the training and validation sets, respectively. The decision curves of the radiomics nomogram and radiomics signature in the validation set indicated an overall net benefit over the clinical factors model.
Our radiomics nomogram can effectively predict the preoperative diagnosis of ROs and may therefore be of assistance in sparing unnecessary surgery and tailoring precise therapy. The ROC curves of the clinical model, the radiomics signature and the radiomics nomogram for the validation set. RO = Renal oncocytoma; ccRCC = Clear cell renal cell carcinoma.
肾嗜酸细胞瘤（RO）是因被误诊为肾细胞癌而最常接受手术切除的良性肾肿瘤。这种误诊通常归因于影像学特征重叠。本研究描述了基于临床数据和影像组学特征构建影像组学列线图，用于在三相增强CT上对RO与透明细胞肾细胞癌（ccRCC）进行术前鉴别。
共纳入122例患有RO（n = 46）或ccRCC（n = 76）的患者（训练集85例，外部验证集37例）。评估患者特征和三相增强CT影像特征以构建临床因素模型。通过从三相增强CT图像中提取影像组学特征构建影像组学特征，并计算影像组学评分（Rad-score）。然后根据多变量逻辑回归分析，将Rad-score和重要临床因素纳入构建影像组学列线图。在训练集和验证集中评估上述三种模型的诊断性能。
选择中央星状区域和肾周筋膜增厚构建临床因素模型。结合11个影像组学特征构建影像组学特征。基于所选临床因素和Rad-score的影像组学列线图在训练集和验证集中的AUC分别为0.960和0.898。验证集中影像组学列线图和影像组学特征的决策曲线表明，总体净效益优于临床因素模型。
我们的影像组学列线图可有效预测RO的术前诊断，因此可能有助于避免不必要的手术并制定精准治疗方案。验证集的临床模型、影像组学特征和影像组学列线图的ROC曲线。RO = 肾嗜酸细胞瘤；ccRCC = 透明细胞肾细胞癌。
