Nedrud Marybeth, Wolfson Tanya, Allen Brian, Aslam Anum, Burke Lauren, Chernyak Victoria, Fowler Kathryn, Fraum Tyler J, Ha Hong-Il, Hecht Elizabeth M, Jaffe Tracy, Kalisz Kevin, Siobhan Kierans Andrea, Ludwig Daniel R, Makkar Jasnit S, McGinty Katrina, McInnes Matthew, Mendiratta-Lala Mishal, Oloruntoba Omobonike, Ranathunga Damithri, Wildman-Tobriner Benjamin, Gamst Anthony C, Cardona Diana M, Muir Andrew, Bashir Mustafa
Duke University, Durham, USA.
University of California, San Diego, San Diego, USA.
Abdom Radiol (NY). 2025 Apr 28. doi: 10.1007/s00261-025-04960-6.
The Liver Imaging Reporting and Data System (LI-RADS, LR) provides a framework for diagnosing hepatocellular carcinoma (HCC, LR-5). However, not all HCCs meet LR-5 criteria and are instead categorized as LR-M, probably or definitely malignant but not specific for HCC, necessitating biopsy for diagnosis. The purpose is to identify factors associated with HCC in LR-M observations.
This is an IRB-approved, retrospective analysis of participants from 8 institutions that had a LR-M observation on CT or MRI with corresponding histopathologic diagnosis. Demographics and biochemical data were examined. Central review using the LI-RADS v2018 algorithm was performed. Kappa statistics defined inter-reader agreement. Random forest and logistic regression analyses generated a model for HCC diagnosis.
162 participants with 162 LR-M observations were included. 46% of observations (74/162) were HCC and 37% were cholangiocarcinoma (60/162). Two of 34 imaging features- observation size and intra-lesion iron- showed moderate to strong inter-reader agreement (Kappa ≥ 0.60) while the remainder showed weak or no agreement (Kappa < 0.60). Random forest analysis showed biochemical features to be more predictive of HCC than imaging features. Logistic regression analysis of a model based on INR and AFP provided a 72% sensitivity and 61% specificity for HCC by Youden's index and a 90% specificity threshold yielded 38% sensitivity, 75% positive predictive value, and 66% negative predictive value.
Our results show INR and AFP are associated with HCC in LR-M observations. A high-specificity threshold may assist in the non-invasive diagnosis of HCC in the appropriate setting. In certain at-risk patients with a LR-M observation on diagnostic imaging, serum AFP and INR maybe useful tools for the non-invasive diagnosis of HCC.
肝脏影像报告和数据系统（LI-RADS，LR）为肝细胞癌（HCC，LR-5）的诊断提供了一个框架。然而，并非所有HCC都符合LR-5标准，而是被归类为LR-M，可能或肯定为恶性，但并非HCC所特有，因此需要活检来进行诊断。目的是确定与LR-M观察结果中HCC相关的因素。
这是一项经机构审查委员会批准的回顾性分析，研究对象来自8家机构，这些机构对CT或MRI进行了LR-M观察，并进行了相应的组织病理学诊断。检查了人口统计学和生化数据。使用LI-RADS v2018算法进行中心审查。kappa统计定义了阅片者间的一致性。随机森林和逻辑回归分析生成了一个HCC诊断模型。
纳入了162名有162次LR-M观察结果的参与者。46%的观察结果（74/162）为HCC，37%为胆管癌（60/162）。34项影像特征中的两项——观察大小和病灶内铁含量——显示阅片者间有中度至高度一致性（kappa≥0.60），而其余特征显示一致性较弱或无一致性（kappa<0.60）。随机森林分析表明，生化特征比影像特征更能预测HCC。基于国际标准化比值（INR）和甲胎蛋白（AFP）的模型进行逻辑回归分析，根据约登指数，对HCC的敏感性为72%，特异性为61%；特异性阈值为90%时，敏感性为38%，阳性预测值为75%，阴性预测值为66%。
我们的结果表明，在LR-M观察结果中，INR和AFP与HCC相关。高特异性阈值可能有助于在适当情况下对HCC进行非侵入性诊断。在某些诊断性影像显示为LR-M的高危患者中，血清AFP和INR可能是非侵入性诊断HCC的有用工具。
