Gao Mengmeng, Li Shichao, Yuan Guanjie, Qu Weinuo, He Kangwen, Liao Zhouyan, Yin Ting, Chen Wei, Chu Qian, Li Zhen
Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
MR Research Collaboration Team, Siemens Healthineers Ltd, Chengdu, China.
Eur Radiol Exp. 2024 Dec 5;8(1):135. doi: 10.1186/s41747-024-00537-y.
To explore the value of three-dimensional arterial spin labeling (ASL) and six diffusion magnetic resonance imaging (MRI) models in differentiating solid benign and malignant renal tumors.
This retrospective study included 89 patients with renal tumors. All patients underwent ASL and ZOOMit diffusion-weighted imaging (DWI) examinations and were divided into three groups: clear cell renal cell carcinoma (ccRCC), non-ccRCC, and benign renal tumors (BRT). The mean and peak renal blood flow (RBFmean and RBFpeak) from ASL and fourteen diffusion parameters from mono-exponential DWI (Mono_DWI), intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI), stretched exponential model (SEM), fractional order calculus (FROC), and continuous-time random-walk (CTRW) model were analyzed. Binary logistic regression was used to determine the optimal parameter combinations. The diagnostic performance of various MRI-derived parameters and their combinations was compared.
Among the six diffusion models, the SEM model achieved the highest performance in differentiating ccRCC from non-ccRCC (area under the receiver operating characteristic curve [AUC] 0.880) and from BRT (AUC 0.891). IVIM model achieved the highest AUC (0.818) in differentiating non-ccRCC from BRT. Among all the MRI-derived parameters, RBFpeak combined with DKI_MK yielded the highest AUC (0.970) in differentiating ccRCC from non-ccRCC, and the combination of RBFpeak, SEM_DDC, and FROC_μ yielded the highest AUC (0.992) for differentiating ccRCC from BRT.
ASL and all diffusion models showed similar diagnostic performance in differentiating ccRCC from non-ccRCC or BRT, while the IVIM model performed better in distinguishing non-ccRCC from BRT. Combining ASL with diffusion models can provide additional value in predicting ccRCC.
Considering the increasing detection rate of incidental renal masses, accurate discrimination of benign and malignant renal tumors is crucial for decision-making. Combining ASL with diffusion MRI models offers a promising solution to this clinical issue.
All assessed models were effective for differentiating ccRCC from non-ccRCC or BRT. ASL and all diffusion models showed similar performance in differentiating ccRCC from non-ccRCC or BRT. Combining ASL with diffusion models significantly improved diagnostic efficacy in predicting ccRCC. IVIM model could better differentiate non-ccRCC from BRT.
探讨三维动脉自旋标记（ASL）和六种扩散磁共振成像（MRI）模型在鉴别实性肾良恶性肿瘤中的价值。
这项回顾性研究纳入了89例肾肿瘤患者。所有患者均接受了ASL和ZOOMit扩散加权成像（DWI）检查，并分为三组：透明细胞肾细胞癌（ccRCC）、非ccRCC和良性肾肿瘤（BRT）。分析了ASL的平均和峰值肾血流量（RBFmean和RBFpeak）以及单指数DWI（Mono_DWI）、体素内不相干运动（IVIM）、扩散峰度成像（DKI）、拉伸指数模型（SEM）、分数阶微积分（FROC）和连续时间随机游走（CTRW）模型的14个扩散参数。采用二元逻辑回归确定最佳参数组合。比较了各种MRI衍生参数及其组合的诊断性能。
在六种扩散模型中，SEM模型在区分ccRCC与非ccRCC（受试者操作特征曲线下面积[AUC]为0.880）以及与BRT（AUC为0.891）方面表现最佳。IVIM模型在区分非ccRCC与BRT方面的AUC最高（0.818）。在所有MRI衍生参数中，RBFpeak与DKI_MK相结合在区分ccRCC与非ccRCC方面的AUC最高（0.970），而RBFpeak、SEM_DDC和FROC_μ的组合在区分ccRCC与BRT方面的AUC最高（0.992）。
ASL和所有扩散模型在区分ccRCC与非ccRCC或BRT方面表现出相似的诊断性能，而IVIM模型在区分非ccRCC与BRT方面表现更好。将ASL与扩散模型相结合可以在预测ccRCC方面提供额外价值。
考虑到偶然发现的肾肿块检出率不断增加，准确鉴别肾良恶性肿瘤对于决策至关重要。将ASL与扩散MRI模型相结合为这一临床问题提供了一个有前景的解决方案。
所有评估模型在区分ccRCC与非ccRCC或BRT方面均有效。ASL和所有扩散模型在区分ccRCC与非ccRCC或BRT方面表现出相似的性能。将ASL与扩散模型相结合显著提高了预测ccRCC的诊断效能。IVIM模型能更好地区分非ccRCC与BRT。
