Radiology Department, Rangueil University Hospital, Toulouse, France.
Department of Radiology, New York Presbyterian Hospital, Columbia University Vagelos College of Physicians and Surgeons, New York City, NY, USA.
Eur Radiol. 2020 Jan;30(1):558-570. doi: 10.1007/s00330-019-06347-w. Epub 2019 Aug 23.
To enhance clinician's decision-making by diagnosing hepatocellular carcinoma (HCC) in cirrhotic patients with indeterminate liver nodules using quantitative imaging features extracted from triphasic CT scans.
We retrospectively analyzed 178 cirrhotic patients from 27 institutions, with biopsy-proven liver nodules classified as indeterminate using the European Association for the Study of the Liver (EASL) guidelines. Patients were randomly assigned to a discovery cohort (142 patients (pts.)) and a validation cohort (36 pts.). Each liver nodule was segmented on each phase of triphasic CT scans, and 13,920 quantitative imaging features (12 sets of 1160 features each reflecting the phenotype at one single phase or its change between two phases) were extracted. Using machine-learning techniques, the signature was trained and calibrated (discovery cohort), and validated (validation cohort) to classify liver nodules as HCC vs. non-HCC. Effects of segmentation and contrast enhancement quality were also evaluated.
Patients were predominantly male (88%) and CHILD A (65%). Biopsy was positive for HCC in 77% of patients. LI-RADS scores were not different between HCC and non-HCC patients. The signature included a single radiomics feature quantifying changes between arterial and portal venous phases: V-ADWT1_LL_Variance-2D and reached area under the receiver operating characteristic curve (AUC) of 0.70 (95%CI 0.61-0.80) and 0.66 (95%CI 0.64-0.84) in discovery and validation cohorts, respectively. The signature was influenced neither by segmentation nor by contrast enhancement.
A signature using a single feature was validated in a multicenter retrospective cohort to diagnose HCC in cirrhotic patients with indeterminate liver nodules. Artificial intelligence could enhance clinicians' decision by identifying a subgroup of patients with high HCC risk.
• In cirrhotic patients with visually indeterminate liver nodules, expert visual assessment using current guidelines cannot accurately differentiate HCC from differential diagnoses. Current clinical protocols do not entail biopsy due to procedural risks. Radiomics can be used to non-invasively diagnose HCC in cirrhotic patients with indeterminate liver nodules, which could be leveraged to optimize patient management. • Radiomics features contributing the most to a better characterization of visually indeterminate liver nodules include changes in nodule phenotype between arterial and portal venous phases: the "washout" pattern appraised visually using EASL and EASL guidelines. • A clinical decision algorithm using radiomics could be applied to reduce the rate of cirrhotic patients requiring liver biopsy (EASL guidelines) or wait-and-see strategy (AASLD guidelines) and therefore improve their management and outcome.
通过提取三期 CT 扫描中定量成像特征，为肝硬化患者中疑似肝脏结节提供诊断肝细胞癌（HCC）的临床决策支持。
我们回顾性分析了 27 家机构的 178 例肝硬化患者，根据欧洲肝脏研究协会（EASL）指南，活检证实的肝脏结节为疑似 HCC。患者被随机分为发现队列（142 例患者）和验证队列（36 例）。对三期 CT 扫描的每个肝脏结节进行分割，并提取 13920 个定量成像特征（12 组，每组 1160 个特征，分别反映单期的表型或两期之间的变化）。使用机器学习技术，对特征进行训练和校准（发现队列），并进行验证（验证队列），以对 HCC 和非 HCC 肝脏结节进行分类。还评估了分割和对比增强质量的影响。
患者主要为男性（88%）和 Child-Pugh A 级（65%）。77%的患者活检结果为 HCC 阳性。LI-RADS 评分在 HCC 和非 HCC 患者之间没有差异。该特征包括一个定量评估动脉期和门静脉期之间变化的放射组学特征：V-ADWT1_LL_Variance-2D，在发现队列和验证队列中的曲线下面积（AUC）分别为 0.70（95%CI 0.61-0.80）和 0.66（95%CI 0.64-0.84）。该特征不受分割或对比增强的影响。
在多中心回顾性队列中，使用单一特征的特征集验证了对肝硬化患者中疑似肝脏结节的 HCC 诊断。人工智能可以通过识别具有高 HCC 风险的患者亚组来增强临床医生的决策能力。
在具有视觉不确定性的肝硬化患者中，使用当前指南的专家视觉评估无法准确区分 HCC 与其他鉴别诊断。由于操作风险，当前的临床方案不要求进行活检。放射组学可用于非侵入性诊断肝硬化患者中具有视觉不确定性的肝脏结节，这可能有助于优化患者管理。
对视觉不确定性肝脏结节的特征描述贡献最大的放射组学特征包括动脉期和门静脉期之间结节表型的变化：EASL 和 EASL 指南中评估的“洗脱”模式。
使用放射组学的临床决策算法可用于降低需要肝活检的肝硬化患者（EASL 指南）或观望策略（AASLD 指南）的比率，从而改善他们的管理和结果。
