Abstract：
To evaluate hepatocellular adenoma (HCA) subtyping using qualitative magnetic resonance imaging (MRI) features and feasibility of differentiating HCA subtypes using machine learning (ML) of qualitative and quantitative MRI features with histopathology as the reference standard.
This retrospective study included 39 histopathologically subtyped HCAs (13 hepatocyte nuclear factor (HNF)-1-alpha mutated [HHCA], 11 inflammatory [IHCA], one beta-catenin-mutated [BHCA], and 14 unclassified [UHCA]) in 36 patients. HCA subtyping by two blinded radiologists using the proposed schema of qualitative MRI features and using the random forest algorithm was compared against histopathology. For quantitative features, 1,409 radiomic features were extracted after segmentation and reduced to 10 principle components. Support vector machine and logistic regression was applied to assess HCA subtyping.
Qualitative MRI features with proposed flow chart yielded diagnostic accuracies of 87%, 82%, and 74% for HHCA, IHCA, and UHCA respectively. The ML algorithm based on qualitative MRI features showed AUCs (area under the receiver operating characteristic curve [ROC] curve) of 0.846, 0.642, and 0.766 for diagnosing HHCA, IHCA, and UHCA, respectively. Quantitative radiomic features from portal venous and hepatic venous phase MRI demonstrated AUCs of 0.83 and 0.82, with a sensitivity of 72% and a specificity of 85% in predicting HHCA subtype.
The proposed schema of integrated qualitative MRI features with ML algorithm provided high accuracy for HCA subtyping while quantitative radiomic features provide value for diagnosis of HHCA. The key qualitative MRI features for differentiating HCA subtypes were concordant between the radiologists and the ML algorithm. These approaches appear promising to better inform clinical management for patients with HCA.
This retrospective study included 39 histopathologically subtyped HCAs (13 hepatocyte nuclear factor (HNF)-1-alpha mutated [HHCA], 11 inflammatory [IHCA], one beta-catenin-mutated [BHCA], and 14 unclassified [UHCA]) in 36 patients. HCA subtyping by two blinded radiologists using the proposed schema of qualitative MRI features and using the random forest algorithm was compared against histopathology. For quantitative features, 1,409 radiomic features were extracted after segmentation and reduced to 10 principle components. Support vector machine and logistic regression was applied to assess HCA subtyping.
Qualitative MRI features with proposed flow chart yielded diagnostic accuracies of 87%, 82%, and 74% for HHCA, IHCA, and UHCA respectively. The ML algorithm based on qualitative MRI features showed AUCs (area under the receiver operating characteristic curve [ROC] curve) of 0.846, 0.642, and 0.766 for diagnosing HHCA, IHCA, and UHCA, respectively. Quantitative radiomic features from portal venous and hepatic venous phase MRI demonstrated AUCs of 0.83 and 0.82, with a sensitivity of 72% and a specificity of 85% in predicting HHCA subtype.
The proposed schema of integrated qualitative MRI features with ML algorithm provided high accuracy for HCA subtyping while quantitative radiomic features provide value for diagnosis of HHCA. The key qualitative MRI features for differentiating HCA subtypes were concordant between the radiologists and the ML algorithm. These approaches appear promising to better inform clinical management for patients with HCA.
摘要：
目的：使用定性磁共振成像（MRI）特征评估肝细胞腺瘤（HCA）亚型，并以组织病理学为参考标准，使用定性和定量MRI特征的机器学习（ML）区分HCA亚型的可行性。
方法：这项回顾性研究包括39个组织病理学亚型HCA（13个肝细胞核因子（HNF）-1-α突变[HHCA]，11炎症[IHCA]，一个β-连环蛋白突变[BHCA]，和36例患者中的14例未分类[UHCA]）。使用所提出的定性MRI特征的模式并使用随机森林算法，将两名盲放射科医生的HCA亚型与组织病理学进行了比较。对于定量特征，分割后提取了1,409个放射学特征，并将其简化为10个主要成分。应用支持向量机和逻辑回归评估HCA亚型。
结果：定性MRI特征与建议的流程图产生了87%的诊断准确率，82%,和74%的HHCA，IHCA,分别为UHCA。基于定性MRI特征的ML算法显示用于诊断HHCA的AUC（受试者工作特征曲线[ROC]曲线下面积）为0.846、0.642和0.766，IHCA,UHCA,分别。门静脉和肝静脉期MRI的定量影像学特征显示AUC为0.83和0.82，预测HHCA亚型的敏感性为72％，特异性为85％。
结论：提出的整合定性MRI特征与ML算法的方案为HCA亚型分型提供了较高的准确性，而定量影像特征为HHCA的诊断提供了价值。区分HCA亚型的关键定性MRI特征在放射科医生和ML算法之间是一致的。这些方法似乎有望更好地为HCA患者的临床管理提供信息。
方法：这项回顾性研究包括39个组织病理学亚型HCA（13个肝细胞核因子（HNF）-1-α突变[HHCA]，11炎症[IHCA]，一个β-连环蛋白突变[BHCA]，和36例患者中的14例未分类[UHCA]）。使用所提出的定性MRI特征的模式并使用随机森林算法，将两名盲放射科医生的HCA亚型与组织病理学进行了比较。对于定量特征，分割后提取了1,409个放射学特征，并将其简化为10个主要成分。应用支持向量机和逻辑回归评估HCA亚型。
结果：定性MRI特征与建议的流程图产生了87%的诊断准确率，82%,和74%的HHCA，IHCA,分别为UHCA。基于定性MRI特征的ML算法显示用于诊断HHCA的AUC（受试者工作特征曲线[ROC]曲线下面积）为0.846、0.642和0.766，IHCA,UHCA,分别。门静脉和肝静脉期MRI的定量影像学特征显示AUC为0.83和0.82，预测HHCA亚型的敏感性为72％，特异性为85％。
结论：提出的整合定性MRI特征与ML算法的方案为HCA亚型分型提供了较高的准确性，而定量影像特征为HHCA的诊断提供了价值。区分HCA亚型的关键定性MRI特征在放射科医生和ML算法之间是一致的。这些方法似乎有望更好地为HCA患者的临床管理提供信息。
