A CT-based radiomics nomogram for differentiation of focal nodular hyperplasia from hepatocellular carcinoma in the non-cirrhotic liver
A CT-based radiomics nomogram for differentiation of focal nodular hyperplasia from hepatocellular carcinoma in the non-cirrhotic liver
复制标题
基于 CT 的放射组学列线图用于区分非肝硬化肝脏中局灶性结节性增生与肝细胞癌
DOI：
10.1186/s40644-020-00297-z
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发表时间：
2020-02-24
期刊：
影响因子：
4.9
通讯作者：
Xu, Wenjian
中科院分区：
文献类型：
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作者：
Nie, Pei;Yang, Guangjie;Xu, Wenjian
本研究的目的是开发和验证一种放射组学图，用于术前区分非肝硬化肝脏的局灶性结节性增生（FNH）和肝细胞癌（HCC）。方法156例FNH合并HCC患者（n= 101）分为训练组（n= 119）和验证组（n= 37）。从三相对比CT图像中提取放射组学特征。采用最小绝对收缩和选择算子算法构建放射组学特征，并计算放射组学评分（Rad-score）。评估临床资料和CT表现，建立临床因素模型。结合rad评分和独立临床因素，通过多因素logistic回归分析构建放射组学线图。从辨别性和临床实用性方面评估Nomogram表现。结果共提取了42,227个特征，并将其简化为10个特征，作为构建放射组学特征的最重要判别符。放射组学特征在训练集（AUC[曲线下面积]，0.964；95%可信区间[CI]， 0.934-0.995）和验证集（AUC, 0.865; 95% CI, 0.725-1.000）中具有良好的辨别能力。年龄、乙型肝炎病毒感染、增强方式是独立的临床因素。结合rad评分和临床因素的放射组学模式图在训练集（AUC, 0.979; 95% CI, 0.959-0.998）和验证集（AUC, 0.917; 95% CI, 0.800-1.000）中具有较好的判别能力（P< 0.001），且与临床因素模型（AUC, 0.799; 95% CI, 0.719-0.879）相比，在训练集中表现出更好的判别能力（P< 0.001）。决策曲线分析显示，nomogram临床可用性优于临床因素模型。结论基于ct的放射组学图是一种结合rad评分和临床因素的无创术前预测工具，对非肝硬化肝脏中FNH与HCC的鉴别具有良好的预测效果，可能有助于临床决策。
BackgroundThe 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.MethodsA 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.ResultsFour 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.ConclusionsThe 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.