Department of Radiology, Shinshu University School of Medicine, Matsumoto, Nagano, Japan.
Division of Gastroenterological, Hepato-Biliary-Pancreatic, Transplantation and Pediatric Surgery, Department of Surgery, Shinshu University School of Medicine, Matsumoto, Nagano, Japan.
Cancer Med. 2023 Apr;12(7):8018-8026. doi: 10.1002/cam4.5589. Epub 2023 Jan 22.
Using classification tree analysis, we evaluated the most useful magnetic resonance (MR) image type in the differentiation between early and progressed hepatocellular carcinoma (eHCC and pHCC).
We included pathologically proven 214 HCCs (28 eHCCs and 186 pHCCs) in 144 patients. The signal intensity of HCCs was assessed on in-phase (T1in) and opposed-phase T1-weighted images (T1op), ultrafast T2-weighted images (ufT2WI), fat-saturated T2-weighted images (fsT2WI), diffusion-weighted images (DWI), contrast enhanced T1-weighted images in the arterial phase (AP), portal venous phase (PVP), and the hepatobiliary phase. Fat content and washout were also evaluated. Fisher's exact test was performed to evaluate usefulness for the differentiation. Then, we chose MR images using binary logistic regression analysis and performed classification and regression tree analysis with them. Diagnostic performances of the classification tree were evaluated using a stratified 10-fold cross-validation method.
T1in, ufT2WI, fsT2WI, DWI, AP, PVP, fat content, and washout were all useful for the differentiation (p < 0.05), and AP and T1in were finally chosen for creating classification trees (p < 0.05). AP appeared in the first node in the tree. The area under the curve, sensitivity and specificity for eHCC, and balanced accuracy of the classification tree were 0.83 (95% CI 0.74-0.91), 0.64 (18/28, 95% CI 0.46-0.82), 0.94 (174/186, 95% CI 0.90-0.97), and 0.79 (95% CI 0.70-0.87), respectively.
AP is the most useful MR image type and T1in the second in the differentiation between eHCC and pHCC.
通过分类树分析，我们评估最有用的磁共振（MR）图像类型，以区分早期和进展期肝细胞癌（eHCC 和 pHCC）。
我们纳入了 144 名患者的 214 例经病理证实的 HCC（28 例 eHCC 和 186 例 pHCC）。在同相位（T1in）和反相位 T1 加权图像（T1op）、超快 T2 加权图像（ufT2WI）、脂肪饱和 T2 加权图像（fsT2WI）、弥散加权图像（DWI）、动脉期（AP）、门静脉期（PVP）和肝胆期增强 T1 加权图像上评估 HCC 的信号强度。还评估了脂肪含量和洗脱情况。使用 Fisher 确切检验评估其对区分的有用性。然后，我们使用二元逻辑回归分析选择 MR 图像，并对其进行分类和回归树分析。使用分层 10 折交叉验证方法评估分类树的诊断性能。
T1in、ufT2WI、fsT2WI、DWI、AP、PVP、脂肪含量和洗脱均对区分有用（p<0.05），最终选择 AP 和 T1in 用于创建分类树（p<0.05）。AP 出现在树的第一个节点中。分类树的曲线下面积、eHCC 的敏感性和特异性以及分类树的平衡准确性分别为 0.83（95%CI 0.74-0.91）、0.64（28/43，95%CI 0.46-0.82）、0.94（186/199，95%CI 0.90-0.97）和 0.79（95%CI 0.70-0.87）。
AP 是区分 eHCC 和 pHCC 最有用的 MR 图像类型，T1in 是第二重要的图像类型。
