Declaux Guillaume, Denis de Senneville Baudouin, Trillaud Hervé, Bioulac-Sage Paulette, Balabaud Charles, Blanc Jean-Frédéric, Facq Laurent, Frulio Nora
Service d'imagerie diagnostique et Interventionnelle, centre médicochirurgical Magellan, hôpital Saint-André, centre hospitalier universitaire de Bordeaux, 33000, Bordeaux, France.
Université de Bordeaux, CNRS, Inria, Bordeaux INP, IMB, UMR 5251, 33400, Talence, France.
Res Diagn Interv Imaging. 2024 Apr 5;10:100046. doi: 10.1016/j.redii.2024.100046. eCollection 2024 Jun.
Non-invasive subtyping of hepatocellular adenomas (HCA) remains challenging for several subtypes, thus carrying different levels of risks and management. The goal of this study is to devise a multivariable diagnostic model based on basic clinical features (age and sex) combined with MRI-radiomics and to evaluate its diagnostic performance.
This single-center retrospective case-control study included all consecutive patients with HCA identified within the pathological database from our institution from January 2003 to April 2018 with MRI examination (T2, T1-no injection/injection-arterial-portal); volumes of interest were manually delineated in adenomas and 38 textural features were extracted (LIFEx, v5.10). Qualitative (i.e., visual on MRI) and automatic (computer-assisted) analysis were compared. The prognostic scores of a multivariable diagnostic model based on basic clinical features (age and sex) combined with MRI-radiomics (tumor volume and texture features) were assessed using a cross-validated Random Forest algorithm.
Via visual MR-analysis, HCA subgroups could be classified with balanced accuracies of 80.8 % (I-HCA or ß-I-HCA, the two being indistinguishable), 81.8 % (H-HCA) and 74.4 % (sh-HCA or ß-HCA also indistinguishable). Using a model including age, sex, volume and texture variables, HCA subgroups were predicted (multivariate classification) with an averaged balanced accuracy of 58.6 %, best=73.8 % (sh-HCA) and 71.9 % (ß-HCA). I-HCA and ß-I-HCA could be also distinguished (binary classification) with a balanced accuracy of 73 %.
Multiple HCA subtyping could be improved using machine-learning algorithms including two clinical features, i.e., age and sex, combined with MRI-radiomics. Future HCA studies enrolling more patients will further test the validity of the model.
肝细胞腺瘤（HCA）的无创亚型分类对几种亚型来说仍然具有挑战性，因此具有不同程度的风险和管理方式。本研究的目的是设计一种基于基本临床特征（年龄和性别）并结合MRI放射组学的多变量诊断模型，并评估其诊断性能。
这项单中心回顾性病例对照研究纳入了2003年1月至2018年4月在我们机构病理数据库中确诊的所有连续HCA患者，并进行了MRI检查（T2、T1-未注射/注射-动脉期-门静脉期）；在腺瘤中手动勾勒感兴趣区域，并提取38个纹理特征（LIFEx，v5.10）。比较了定性（即MRI上的视觉分析）和自动（计算机辅助）分析。使用交叉验证的随机森林算法评估基于基本临床特征（年龄和性别）并结合MRI放射组学（肿瘤体积和纹理特征）的多变量诊断模型的预后评分。
通过视觉MR分析，HCA亚组的分类平衡准确率分别为80.8%（I-HCA或β-I-HCA，两者无法区分）、81.8%（H-HCA）和74.4%（sh-HCA或β-HCA也无法区分）。使用包含年龄、性别、体积和纹理变量的模型，预测HCA亚组（多变量分类）的平均平衡准确率为58.6%，最佳为73.8%（sh-HCA）和71.9%（β-HCA）。I-HCA和β-I-HCA也可以通过二元分类进行区分，平衡准确率为73%。
使用包括年龄和性别这两个临床特征并结合MRI放射组学的机器学习算法，可以改善多种HCA亚型的分类。未来纳入更多患者的HCA研究将进一步检验该模型的有效性。
