Department of Radiology, Zhejiang University School of Medicine First Affiliated Hospital, No.79 Qingchun Road, Hangzhou 310003, China.
Department of Radiology, Shulan (Hangzhou) Hospital, Affiliated to Shulan International Medical College, Zhejiang Shuren University, Hangzhou 310003, China.
Eur J Radiol. 2023 May;162:110770. doi: 10.1016/j.ejrad.2023.110770. Epub 2023 Mar 14.
To develop and validate an effective algorithm, based on classification and regression tree (CART) analysis and LI-RADS features, for diagnosing HCC ≤ 3.0 cm with gadoxetate disodium‑enhanced MRI (Gd-EOB-MRI).
We retrospectively included 299 and 90 high-risk patients with hepatic lesions ≤ 3.0 cm that underwent Gd-EOB-MRI from January 2018 to February 2021 in institution 1 (development cohort) and institution 2 (validation cohort), respectively. Through binary and multivariate regression analyses of LI-RADS features in the development cohort, we developed an algorithm using CART analysis, which comprised the targeted appearance and independently significant imaging features. On per-lesion basis, we compared the diagnostic performances of our algorithm, two previously reported CART algorithms, and LI-RADS LR-5 in development and validation cohorts.
Our CART algorithm, presenting as a decision tree, included targetoid appearance, HBP hypointensity, nonrim arterial phase hyperenhancement (APHE), and transitional phase hypointensity plus mild-moderate T2 hyperintensity. For definite HCC diagnosis, the overall sensitivity of our algorithm (development cohort 93.2%, validation cohort 92.5%; P < 0.006) was significantly higher than those of Jiang's algorithm modified LR-5 (defined as targetoid appearance, nonperipheral washout, restricted diffusion, and nonrim APHE) and LI-RADS LR-5, with the comparable specificity (development cohort: 84.3%, validation cohort: 86.7%; P ≥ 0.006). Our algorithm, providing the highest balanced accuracy (development cohort: 91.2%, validation cohort: 91.6%), outperformed other criteria for identifying HCCs from non-HCC lesions.
In high-risk patients, our CART algorithm developed with LI-RADS features showed promise for the early diagnosis of HCC ≤ 3.0 cm with Gd-EOB-MRI.
基于分类回归树（CART）分析和 LI-RADS 特征，开发并验证一种用于诊断直径≤3.0cm 的肝细胞癌（HCC）的有效算法，其诊断所采用的检查方法为钆塞酸二钠增强磁共振成像（Gd-EOB-MRI）。
我们回顾性纳入了分别于 2018 年 1 月至 2021 年 2 月在机构 1（开发队列）和机构 2（验证队列）接受 Gd-EOB-MRI 检查的直径≤3.0cm 的肝脏病变高危患者 299 例和 90 例。通过对开发队列中 LI-RADS 特征进行二分类和多变量回归分析，我们应用 CART 分析开发了一种算法，该算法包括靶向外观和独立的显著影像学特征。基于每例病变，我们比较了我们的算法、两种先前报道的 CART 算法以及 LI-RADS LR-5 在开发和验证队列中的诊断性能。
我们的 CART 算法表现为决策树，其中包括靶样外观、HBP 低信号强度、非环形动脉期高增强（APHE）和过渡期低信号强度加轻度至中度 T2 高信号强度。对于明确 HCC 的诊断，我们的算法（开发队列的总体敏感性为 93.2%，验证队列为 92.5%；P<0.006）显著高于 Jiang 算法改良的 LR-5（定义为靶样外观、非外周洗脱、弥散受限和非环形 APHE）和 LI-RADS LR-5，特异性相当（开发队列：84.3%，验证队列：86.7%；P≥0.006）。我们的算法提供了最高的平衡准确性（开发队列：91.2%，验证队列：91.6%），优于其他用于鉴别 HCC 和非 HCC 病变的标准。
在高危患者中，我们的 LI-RADS 特征 CART 算法在 Gd-EOB-MRI 诊断直径≤3.0cm 的 HCC 方面具有较好的应用前景。
