Division of Medical Imaging and Technology, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, Stockholm, Sweden; Department of Radiology, Karolinska University Hospital Huddinge, Stockholm, Sweden.
Department of Vascular Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China; Department of Interventional Therapy, People's Hospital of Dianjiang County, Chongqing, China.
Eur J Radiol. 2023 Sep;166:111015. doi: 10.1016/j.ejrad.2023.111015. Epub 2023 Jul 27.
To systematically review the efficacy of radiomics models derived from computed tomography (CT) or magnetic resonance imaging (MRI) in preoperative prediction of the histopathological grade of hepatocellular carcinoma (HCC).
Systematic literature search was performed at databases of PubMed, Web of Science, Embase, and Cochrane Library up to 30 December 2022. Studies that developed a radiomics model using preoperative CT/MRI for predicting the histopathological grade of HCC were regarded as eligible. A pre-defined table was used to extract the data related to study and patient characteristics, characteristics of radiomics modelling workflow, and the model performance metrics. Radiomics quality score and the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) were applied for research quality evaluation.
Eleven eligible studies were included in this review, consisting of 2245 patients (range 53-494, median 165). No studies were prospectively designed and only two studies had an external test cohort. Half of the studies (five) used CT images and the other half MRI. The median number of extracted radiomics features was 328 (range: 40-1688), which was reduced to 11 (range: 1-50) after feature selection. The commonly used classifiers were logistic regression and support vector machine (both 4/11). When evaluated on the two external test cohorts, the area under the curve of the radiomics models was 0.70 and 0.77. The median radiomics quality score was 10 (range 2-13), corresponding to 28% (range 6-36%) of the full scale. Most studies showed an unclear risk of bias as evaluated by QUADAS-2.
Radiomics models based on preoperative CT or MRI have the potential to be used as an imaging biomarker for prediction of HCC histopathological grade. However, improved research and reporting quality is required to ensure sufficient reliability and reproducibility prior to implementation into clinical practice.
系统评价基于计算机断层扫描（CT）或磁共振成像（MRI）的影像组学模型在术前预测肝细胞癌（HCC）组织病理学分级中的疗效。
系统检索 PubMed、Web of Science、Embase 和 Cochrane Library 数据库，检索时间截至 2022 年 12 月 30 日。将采用术前 CT/MRI 构建影像组学模型预测 HCC 组织病理学分级的研究纳入合格研究。使用预定义表格提取与研究和患者特征、影像组学模型构建工作流程特征以及模型性能指标相关的数据。采用影像组学质量评分和诊断准确性研究的质量评估 2（QUADAS-2）进行研究质量评价。
本综述纳入 11 项合格研究，共纳入 2245 例患者（范围 53-494，中位数 165）。无前瞻性设计的研究，仅有 2 项研究具有外部测试队列。半数研究（5 项）使用 CT 图像，另一半研究使用 MRI。提取的影像组学特征中位数为 328 个（范围：40-1688），经过特征选择后降至 11 个（范围：1-50）。常用的分类器为逻辑回归和支持向量机（均为 4/11）。在两个外部测试队列中进行评估时，影像组学模型的曲线下面积为 0.70 和 0.77。影像组学质量评分中位数为 10 分（范围 2-13），相当于总分的 28%（范围 6-36%）。大多数研究的 QUADAS-2 评估结果为偏倚风险不明确。
基于术前 CT 或 MRI 的影像组学模型有可能成为预测 HCC 组织病理学分级的影像学生物标志物。但在将其应用于临床实践之前，需要提高研究和报告质量，以确保其具有足够的可靠性和可重复性。
