From the Department of Radiology, University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252 (A.A.P., V.N.K., M.G.L., P.M.G., J.W.G., P.J.P.); and Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (D.C.E., R.M.S.).
Radiology. 2022 Feb;302(2):336-342. doi: 10.1148/radiol.2021210531. Epub 2021 Oct 26.
Background Imaging assessment for hepatomegaly is not well defined and currently uses suboptimal, unidimensional measures. Liver volume provides a more direct measure for organ enlargement. Purpose To determine organ volume and to establish thresholds for hepatomegaly with use of a validated deep learning artificial intelligence tool that automatically segments the liver. Materials and Methods In this retrospective study, liver volumes were successfully derived with use of a deep learning tool for asymptomatic outpatient adults who underwent multidetector CT for colorectal cancer screening (unenhanced) or renal donor evaluation (contrast-enhanced) at a single medical center between April 2004 and December 2016. The performance of the craniocaudal and maximal three-dimensional (3D) linear measures was assessed. The manual liver volume results were compared with the automated results in a subset of renal donors in which the entire liver was included at both precontrast and postcontrast CT. Unenhanced liver volumes were standardized to a postcontrast equivalent, reflecting a correction of 3.6%. Linear regression analysis was performed to assess the major patient-specific determinant or determinants of liver volume among age, sex, height, weight, and body surface area. Results A total of 3065 patients (mean age ± standard deviation, 54 years ± 12; 1639 women) underwent multidetector CT for colorectal screening ( = 1960) or renal donor evaluation ( = 1105). The mean standardized automated liver volume ± standard deviation was 1533 mL ± 375 and demonstrated a normal distribution. Patient weight was the major determinant of liver volume and demonstrated a linear relationship. From this result, a linear weight-based upper limit of normal hepatomegaly threshold volume was derived: hepatomegaly (mL) = 14.0 × (weight [kg]) + 979. A craniocaudal threshold of 19 cm was 71% sensitive (49 of 69 patients) and 86% specific (887 of 1030 patients) for hepatomegaly, and a maximal 3D linear threshold of 24 cm was 78% sensitive (54 of 69) and 66% specific (678 of 1030). In the subset of 189 patients, the median difference in hepatic volume between the deep learning tool and the semiautomated or manual method was 2.3% (38 mL). Conclusion A simple weight-based threshold for hepatomegaly derived by using a fully automated CT-based liver volume segmentation based on deep learning provided an objective and more accurate assessment of liver size than linear measures. © RSNA, 2021 See also the editorial by Sosna in this issue.
背景 肝肿大的影像学评估尚未明确，目前使用的是不理想的、单一维度的测量方法。肝体积提供了一种更直接的器官增大测量方法。目的 利用一种经过验证的深度学习人工智能工具自动对肝脏进行分割，以确定肝脏体积并建立肝肿大的阈值。
材料与方法 本回顾性研究纳入了在 2004 年 4 月至 2016 年 12 月期间于单一医疗中心行多层螺旋 CT 结肠癌筛查（平扫）或肾供者评估（增强）的无症状门诊成年患者，成功利用深度学习工具推导出了这些患者的肝体积。评估了颅尾和最大三维（3D）线性测量的性能。在包括增强前后 CT 的整个肝脏的一部分肾供者中，将手动肝体积结果与自动结果进行了比较。将未增强的肝体积标准化为增强后的等效体积，反映了 3.6%的校正。进行线性回归分析，以评估年龄、性别、身高、体重和体表面积等患者特定因素对肝体积的主要决定因素或决定因素。
结果 共对 3065 例（平均年龄±标准差，54 岁±12；1639 例女性）患者进行了多层螺旋 CT 结肠癌筛查（n=1960）或肾供者评估（n=1105）。平均标准化自动肝体积±标准差为 1533 mL±375，呈正态分布。患者体重是肝体积的主要决定因素，呈线性关系。根据这一结果，推导出了一种基于线性体重的正常肝肿大上限阈值体积：肝肿大（mL）=14.0×（体重[kg]）+979。颅尾阈值为 19 cm 时，肝肿大的敏感度为 71%（69 例患者中的 49 例），特异度为 86%（1030 例患者中的 887 例）；最大 3D 线性阈值为 24 cm 时，敏感度为 78%（69 例患者中的 54 例），特异度为 66%（1030 例患者中的 678 例）。在 189 例患者的亚组中，深度学习工具与半自动或手动方法之间的肝体积中位数差异为 2.3%（38 mL）。
结论 利用基于深度学习的全自动 CT 肝体积分割方法，基于体重简单地为肝肿大建立一个阈值，与线性测量相比，为肝大小提供了一种客观且更准确的评估方法。
（放射学）
