Department of Radiology, the Affiliated Hospital of Qingdao University, No.16, Jiangsu Road, Qingdao, 266000, Shandong, China.
Department of Nuclear Medicine, the Affiliated Hospital of Qingdao University, Qingdao, Shandong, China.
Cancer Imaging. 2020 Feb 24;20(1):20. doi: 10.1186/s40644-020-00297-z.
The purpose of this study was to develop and validate a radiomics nomogram for preoperative differentiating focal nodular hyperplasia (FNH) from hepatocellular carcinoma (HCC) in the non-cirrhotic liver.
A total of 156 patients with FNH (n = 55) and HCC (n = 101) were divided into a training set (n = 119) and a validation set (n = 37). Radiomics features were extracted from triphasic contrast CT images. A radiomics signature was constructed with the least absolute shrinkage and selection operator algorithm, and a radiomics score (Rad-score) was calculated. Clinical data and CT findings were assessed to build a clinical factors model. Combined with the Rad-score and independent clinical factors, a radiomics nomogram was constructed by multivariate logistic regression analysis. Nomogram performance was assessed with respect to discrimination and clinical usefulness.
Four thousand two hundred twenty-seven features were extracted and reduced to 10 features as the most important discriminators to build the radiomics signature. The radiomics signature showed good discrimination in the training set (AUC [area under the curve], 0.964; 95% confidence interval [CI], 0.934-0.995) and the validation set (AUC, 0.865; 95% CI, 0.725-1.000). Age, Hepatitis B virus infection, and enhancement pattern were the independent clinical factors. The radiomics nomogram, which incorporated the Rad-score and clinical factors, showed good discrimination in the training set (AUC, 0.979; 95% CI, 0.959-0.998) and the validation set (AUC, 0.917; 95% CI, 0.800-1.000), and showed better discrimination capability (P < 0.001) compared with the clinical factors model (AUC, 0.799; 95% CI, 0.719-0.879) in the training set. Decision curve analysis showed the nomogram outperformed the clinical factors model in terms of clinical usefulness.
The CT-based radiomics nomogram, a noninvasive preoperative prediction tool that incorporates the Rad-score and clinical factors, shows favorable predictive efficacy for differentiating FNH from HCC in the non-cirrhotic liver, which might facilitate clinical decision-making process.
本研究旨在建立并验证一个针对非肝硬化肝脏局灶性结节性增生（FNH）与肝细胞癌（HCC）的术前鉴别诊断的影像组学列线图。
共纳入 156 例 FNH（n=55）和 HCC（n=101）患者，分为训练集（n=119）和验证集（n=37）。从三期增强 CT 图像中提取影像组学特征。采用最小绝对值收缩和选择算子算法构建影像组学特征签名，并计算影像组学评分（Rad-score）。评估临床数据和 CT 表现以构建临床因素模型。通过多变量逻辑回归分析，结合 Rad-score 和独立临床因素，构建影像组学列线图。采用判别效能和临床实用性评估列线图的性能。
提取 4227 个特征，降维至 10 个最重要的鉴别特征，构建影像组学特征签名。该影像组学特征在训练集（AUC [曲线下面积]，0.964；95%置信区间 [CI]，0.934-0.995）和验证集（AUC，0.865；95%CI，0.725-1.000）中均具有良好的判别能力。年龄、乙型肝炎病毒感染和增强模式是独立的临床因素。纳入 Rad-score 和临床因素的影像组学列线图在训练集（AUC，0.979；95%CI，0.959-0.998）和验证集（AUC，0.917；95%CI，0.800-1.000）中具有良好的判别能力，与训练集的临床因素模型（AUC，0.799；95%CI，0.719-0.879）相比，具有更好的判别能力（P<0.001）。决策曲线分析显示，该列线图在临床实用性方面优于临床因素模型。
该基于 CT 的影像组学列线图是一种非侵入性的术前预测工具，纳入了 Rad-score 和临床因素，对于鉴别非肝硬化肝脏中的 FNH 和 HCC 具有良好的预测效能，有助于临床决策过程。
