Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Department of Anesthesiology, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Diagn Interv Radiol. 2023 Nov 7;29(6):741-752. doi: 10.4274/dir.2023.232215. Epub 2023 Sep 4.
To evaluate the predictive value of a combination model of Liver Imaging Reporting and Data System (LI-RADS)-based magnetic resonance imaging (MRI) and clinicopathologic features to identify atypical hepatocellular carcinoma (HCC) in LI-RADS category M (LR-M) observations.
A total of 105 patients with HCC based on surgery or biopsy who underwent preoperative MRI were retrospectively reviewed in the training group from hospital-1 between December 2016 and November 2020. The LI-RADS-based MRI features and clinicopathologic data were compared between LR-M HCC and non-HCC groups. Univariate and least absolute shrinkage and selection operator regression analyses were used to select the features. Binary logistic regression analysis was then conducted to estimate potential predictors of atypical HCC. A predictive nomogram was established based on the combination of MRI and clinicopathologic features and further validated using an independent external set of data from hospital-2.
Of 113 observations from 105 patients (mean age, 61 years; 77 men) in the training set, 47 (41.59%) were classified as LR-M HCC. Following multivariate analysis, aspartate aminotransferase >40 U/L [odds ratio (OR): 4.65], alpha-fetoprotein >20 ng/mL (OR: 13.04), surface retraction (OR: 0.16), enhancing capsule (OR: 5.24), blood products in mass (OR: 8.2), and iso/hypoenhancement on delayed phase (OR: 10.26) were found to be independently correlated with LR-M HCC. The corresponding area under the curve for a combined model-based nomogram was 0.95 in the training patients (n = 113) and 0.90 in the validation cohort (n = 53).
The combined model incorporating clinicopathologic and MRI features demonstrated a satisfactory prediction result for LR-M HCC.
评估基于肝脏影像报告和数据系统（LI-RADS）的磁共振成像（MRI）与临床病理特征相结合的模型对 LI-RADS 类别 M（LR-M）观察中不典型肝细胞癌（HCC）的预测价值。
回顾性分析 2016 年 12 月至 2020 年 11 月在医院 1 接受手术或活检的 105 例 HCC 患者的术前 MRI 资料，将其分为训练组。比较 LR-M HCC 和非 HCC 组的 LI-RADS 基于 MRI 的特征和临床病理数据。采用单因素和最小绝对收缩和选择算子回归分析筛选特征。然后进行二项逻辑回归分析，以评估不典型 HCC 的潜在预测因子。根据 MRI 和临床病理特征建立预测列线图，并在医院 2 的独立外部数据集中进一步验证。
在训练集中，来自 105 例患者的 113 例观察（平均年龄 61 岁，77 例男性）中，47 例（41.59%）被归类为 LR-M HCC。多因素分析后，天门冬氨酸氨基转移酶＞40 U/L（比值比（OR）：4.65）、甲胎蛋白＞20 ng/mL（OR：13.04）、表面回缩（OR：0.16）、增强包膜（OR：5.24）、肿块内血液制品（OR：8.2）和延迟期等/低增强（OR：10.26）与 LR-M HCC 独立相关。在训练患者（n=113）和验证队列（n=53）中，基于联合模型的列线图的曲线下面积分别为 0.95 和 0.90。
包含临床病理和 MRI 特征的联合模型对 LR-M HCC 的预测结果令人满意。
