Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands (M.P.A.S., W.J.N., S.K., M.G.T.).
Department of Radiology and Nuclear Medicine, Maastricht UMC+, Maastricht, the Netherlands (R.L.M.).
Acad Radiol. 2024 Mar;31(3):870-879. doi: 10.1016/j.acra.2023.07.024. Epub 2023 Aug 28.
Distinguishing malignant from benign liver lesions based on magnetic resonance imaging (MRI) is an important but often challenging task, especially in noncirrhotic livers. We developed and externally validated a radiomics model to quantitatively assess T2-weighted MRI to distinguish the most common malignant and benign primary solid liver lesions in noncirrhotic livers.
Data sets were retrospectively collected from three tertiary referral centers (A, B, and C) between 2002 and 2018. Patients with malignant (hepatocellular carcinoma and intrahepatic cholangiocarcinoma) and benign (hepatocellular adenoma and focal nodular hyperplasia) lesions were included. A radiomics model based on T2-weighted MRI was developed in data set A using a combination of machine learning approaches. The model was internally evaluated on data set A through cross-validation, externally validated on data sets B and C, and compared to visual scoring of two experienced abdominal radiologists on data set C.
The overall data set included 486 patients (A: 187, B: 98, and C: 201). The radiomics model had a mean area under the curve (AUC) of 0.78 upon internal validation on data set A and a similar AUC in external validation (B: 0.74 and C: 0.76). In data set C, the two radiologists showed moderate agreement (Cohen's κ: 0.61) and achieved AUCs of 0.86 and 0.82.
Our T2-weighted MRI radiomics model shows potential for distinguishing malignant from benign primary solid liver lesions. External validation indicated that the model is generalizable despite substantial MRI acquisition protocol differences. Pending further optimization and generalization, this model may aid radiologists in improving the diagnostic workup of patients with liver lesions.
基于磁共振成像（MRI）鉴别肝脏良恶性病变是一项重要但具有挑战性的任务，尤其是在非肝硬化肝脏中。我们开发并外部验证了一个基于 T2 加权 MRI 的放射组学模型，用于定量评估以区分非肝硬化肝脏中最常见的恶性和良性原发性实体性肝脏病变。
数据来自于 2002 年至 2018 年间的三个三级转诊中心（A、B 和 C），纳入恶性（肝细胞癌和肝内胆管细胞癌）和良性（肝细胞腺瘤和局灶性结节性增生）病变患者。在数据集 A 中，我们使用机器学习方法的组合来建立基于 T2 加权 MRI 的放射组学模型。通过交叉验证在数据集 A 中进行内部评估，在数据集 B 和 C 中进行外部验证，并与数据集 C 上两位经验丰富的腹部放射科医生的视觉评分进行比较。
整体数据集共纳入 486 例患者（A：187 例，B：98 例，C：201 例）。在数据集 A 中进行内部验证时，该放射组学模型的曲线下面积（AUC）平均值为 0.78，在外部验证时（B：0.74 和 C：0.76）具有相似的 AUC。在数据集 C 中，两位放射科医生的表现为中度一致（Cohen's κ：0.61），并分别获得了 0.86 和 0.82 的 AUC。
我们的 T2 加权 MRI 放射组学模型在鉴别恶性和良性原发性实体性肝脏病变方面具有潜力。尽管 MRI 采集方案存在很大差异，但外部验证表明该模型具有通用性。在进一步优化和推广后，该模型可能有助于放射科医生改善肝脏病变患者的诊断工作流程。
