Department of Imaging, "G. Mazzini" Hospital, 64100, Teramo, Italy.
Neurocenter of Southern Switzerland, Neuroradiology Department, Ospedale Regionale di Lugano, via Tesserete 46, 6901, Lugano, Switzerland.
Radiol Med. 2022 Aug;127(8):891-898. doi: 10.1007/s11547-022-01516-2. Epub 2022 Jun 28.
To investigate the diagnostic efficacy of MRI diagnostic algorithms with an ascending automatization, in distinguishing between high-grade glioma (HGG) and solitary brain metastases (SBM).
36 patients with histologically proven HGG (n = 18) or SBM (n = 18), matched by size and location were enrolled from a database containing 655 patients. Four different diagnostic algorithms were performed serially to mimic the clinical setting where a radiologist would typically seek out further findings to reach a decision: pure qualitative, analytic qualitative (based on standardized evaluation of tumor features), semi-quantitative (based on perfusion and diffusion cutoffs included in the literature) and a quantitative data-driven algorithm of the perfusion and diffusion parameters. The diagnostic yields of the four algorithms were tested with ROC analysis and Kendall coefficient of concordance.
Qualitative algorithm yielded sensitivity of 72.2%, specificity of 78.8%, and AUC of 0.75. Analytic qualitative algorithm distinguished HGG from SBM with a sensitivity of 100%, specificity of 77.7%, and an AUC of 0.889. The semi-quantitative algorithm yielded sensitivity of 94.4%, specificity of 83.3%, and AUC = 0.889. The data-driven algorithm yielded sensitivity = 94.4%, specificity = 100%, and AUC = 0.948. The concordance analysis between the four algorithms and the histologic findings showed moderate concordance for the first algorithm, (k = 0.501, P < 0.01), good concordance for the second (k = 0.798, P < 0.01), and third (k = 0.783, P < 0.01), and excellent concordance for fourth (k = 0.901, p < 0.0001).
When differentiating HGG from SBM, an analytical qualitative algorithm outperformed qualitative algorithm, and obtained similar results compared to the semi-quantitative approach. However, the use of data-driven quantitative algorithm yielded an excellent differentiation.
研究具有递增自动化的 MRI 诊断算法在鉴别高级别胶质瘤（HGG）和单发脑转移瘤（SBM）方面的诊断效能。
从包含 655 例患者的数据库中，选取经组织学证实的 HGG（n=18）和 SBM（n=18）患者各 18 例，进行配对。连续进行 4 种不同的诊断算法，以模拟临床中放射科医生通常会寻找进一步发现以做出决策的情况：纯定性、分析定性（基于肿瘤特征的标准化评估）、半定量（基于文献中包含的灌注和扩散截止值）和基于灌注和扩散参数的定量数据驱动算法。使用 ROC 分析和 Kendall 一致性系数对四种算法的诊断效能进行检验。
定性算法的敏感性为 72.2%，特异性为 78.8%，AUC 为 0.75。分析定性算法可将 HGG 与 SBM 区分开来，其敏感性为 100%，特异性为 77.7%，AUC 为 0.889。半定量算法的敏感性为 94.4%，特异性为 83.3%，AUC=0.889。数据驱动算法的敏感性为 94.4%，特异性为 100%，AUC=0.948。四种算法与组织学结果之间的一致性分析显示，第一种算法的一致性为中等（k=0.501，P<0.01），第二种算法的一致性为良好（k=0.798，P<0.01），第三种算法的一致性为良好（k=0.783，P<0.01），第四种算法的一致性为极好（k=0.901，P<0.0001）。
在鉴别 HGG 和 SBM 时，分析定性算法优于定性算法，与半定量方法相比获得了相似的结果。然而，使用数据驱动的定量算法可实现出色的鉴别。
