Wu Qian, Yu Yi-Xing, Zhang Tao, Zhu Wen-Jing, Fan Yan-Fen, Wang Xi-Ming, Hu Chun-Hong
Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China.
Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, Nantong, China.
J Magn Reson Imaging. 2023 Apr;57(4):1185-1196. doi: 10.1002/jmri.28391. Epub 2022 Aug 17.
Dual-phenotype hepatocellular carcinoma (DPHCC) is highly aggressive and difficult to distinguish from hepatocellular carcinoma (HCC).
To develop and validate clinical and radiomics models based on contrast-enhanced MRI for the preoperative diagnosis of DPHCC.
Retrospective.
A total of 87 patients with DPHCC and 92 patients with non-DPHCC randomly divided into a training cohort (n = 125: 64 non-DPHCC; 61 DPHCC) and a validation cohort (n = 54: 28 non-DPHCC; 26 DPHCC).
FIELD STRENGTH/SEQUENCE: A 3.0 T; dynamic contrast-enhanced MRI with time-resolved T1-weighted imaging sequence.
In the clinical model, the maximum tumor diameter and hepatitis B virus (HBV) were independent risk factors of DPHCC. In the radiomics model, a total of 1781 radiomics features were extracted from tumor volumes of interest (VOIs) in the arterial phase (AP) and portal venous phase (PP) images. For feature reduction and selection, Pearson correlation coefficient (PCC) and recursive feature elimination (RFE) were used. Clinical, AP, PP, and combined radiomics models were established using machine learning algorithms (support vector machine [SVM], logistic regression [LR], and logistic regression-least absolute shrinkage and selection operator [LR-LASSO]) and their discriminatory efficacy assessed and compared.
The independent sample t test, Mann-Whitney U test, Chi-square test, regression analysis, receiver operating characteristic curve (ROC) analysis, Pearson correlation analysis, the Delong test. A P value < 0.05 was considered statistically significant.
In the validation cohort, the combined radiomics model (area under the curve [AUC] = 0.908, 95% confidence interval [CI]: 0.831-0.985) showed the highest diagnostic performance. The AUCs of the PP (AUC = 0.879, 95% CI: 0.779-0.979) and combined radiomics models were significantly higher than that of clinical model (AUC = 0.685, 95% CI: 0.526-0.844). There were no significant differences in AUC between AP or PP radiomics model and combined radiomics model (P = 0.286, 0.180 and 0.543).
MRI radiomics models may be useful for discriminating DPHCC from non-DPHCC before surgery.
4 TECHNICAL EFFICACY: Stage 2.
双表型肝细胞癌（DPHCC）侵袭性很强，且难以与肝细胞癌（HCC）区分开来。
基于对比增强MRI开发并验证用于DPHCC术前诊断的临床和影像组学模型。
回顾性研究。
共87例DPHCC患者和92例非DPHCC患者，随机分为训练队列（n = 125：64例非DPHCC；61例DPHCC）和验证队列（n = 54：28例非DPHCC；26例DPHCC）。
场强/序列：3.0 T；采用时间分辨T1加权成像序列的动态对比增强MRI。
在临床模型中，最大肿瘤直径和乙型肝炎病毒（HBV）是DPHCC的独立危险因素。在影像组学模型中，从动脉期（AP）和门静脉期（PP）图像的肿瘤感兴趣区（VOI）中提取了总共1781个影像组学特征。为进行特征约简和选择，使用了Pearson相关系数（PCC）和递归特征消除（RFE）。使用机器学习算法（支持向量机[SVM]、逻辑回归[LR]和逻辑回归-最小绝对收缩和选择算子[LR-LASSO]）建立临床、AP、PP和联合影像组学模型，并评估和比较它们的鉴别效能。
独立样本t检验、Mann-Whitney U检验、卡方检验、回归分析、受试者操作特征曲线（ROC）分析、Pearson相关分析、Delong检验。P值<0.05被认为具有统计学意义。
在验证队列中，联合影像组学模型（曲线下面积[AUC]=0.908，95%置信区间[CI]：0.831 - 0.985）显示出最高的诊断性能。PP（AUC = 0.879，95% CI：0.779 - 0.979）和联合影像组学模型的AUC显著高于临床模型（AUC = 0.685，95% CI：0.526 - 0.844）。AP或PP影像组学模型与联合影像组学模型之间的AUC无显著差异（P = 0.286、0.180和0.543）。
MRI影像组学模型可能有助于术前鉴别DPHCC与非DPHCC。
4 技术效能：2级
