Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China.
Department of Radiology, Qingdao Municipal Hospital, Qingdao, Shandong, China.
Br J Radiol. 2022 Jan 1;95(1129):20210534. doi: 10.1259/bjr.20210534. Epub 2021 Nov 4.
Pre-operative differentiation between renal oncocytoma (RO) and chromophobe renal cell carcinoma (chRCC) is critical due to their different clinical behavior and different clinical treatment decisions. The aim of this study was to develop and validate a CT-based radiomics nomogram for the pre-operative differentiation of RO from chRCC.
A total of 141 patients (84 in training data set and 57 in external validation data set) with ROs ( = 47) or chRCCs ( = 94) were included. Radiomics features were extracted from tri-phasic enhanced-CT images. A clinical model was developed based on significant patient characteristics and CT imaging features. A radiomics signature model was developed and a radiomics score (Rad-score) was calculated. A radiomics nomogram model incorporating the Rad-score and independent clinical factors was developed by multivariate logistic regression analysis. The diagnostic performance was evaluated and validated in three models using ROC curves.
Twelve features from CT images were selected to develop the radiomics signature. The radiomics nomogram combining a clinical factor (segmental enhancement inversion) and radiomics signature showed an AUC value of 0.988 in the validation set. Decision curve analysis revealed that the diagnostic performance of the radiomics nomogram was better than the clinical model and the radiomics signature.
The radiomics nomogram combining clinical factors and radiomics signature performed well for distinguishing RO from chRCC.
Differential diagnosis between renal oncocytoma (RO) and chromophobe renal cell carcinoma (chRCC) is rather difficult by conventional imaging modalities when a central scar was present.A radiomics nomogram integrated with the radiomics signature, demographics, and CT findings facilitates differentiation of RO from chRCC with improved diagnostic efficacy.The CT-based radiomics nomogram might spare unnecessary surgery for RO.
术前区分肾嗜酸细胞瘤(RO)和嫌色细胞肾细胞癌(chRCC)非常重要,因为它们具有不同的临床行为和不同的临床治疗决策。本研究旨在开发和验证一种基于 CT 的放射组学列线图,用于术前区分 RO 和 chRCC。
共纳入 141 例 RO(n=47)或 chRCC(n=94)患者(训练数据集 84 例,外部验证数据集 57 例)。从三期增强 CT 图像中提取放射组学特征。基于有意义的患者特征和 CT 影像学特征建立临床模型。建立放射组学特征模型并计算放射组学评分(Rad-score)。通过多变量逻辑回归分析,建立纳入 Rad-score 和独立临床因素的放射组学列线图模型。通过 ROC 曲线在三个模型中评估和验证诊断性能。
从 CT 图像中选择 12 个特征来建立放射组学特征。将临床因素(节段性增强反转)和放射组学特征相结合的放射组学列线图在验证集中的 AUC 值为 0.988。决策曲线分析表明,放射组学列线图的诊断性能优于临床模型和放射组学特征。
结合临床因素和放射组学特征的放射组学列线图在鉴别 RO 和 chRCC 方面表现良好。
当存在中央瘢痕时,常规成像方式对肾嗜酸细胞瘤(RO)和嫌色细胞肾细胞癌(chRCC)的鉴别诊断相当困难。放射组学列线图与放射组学特征、人口统计学和 CT 表现相结合,有助于提高 RO 和 chRCC 的鉴别诊断效能。基于 CT 的放射组学列线图可能为 RO 患者节省不必要的手术。