Perez Alberto A, Noe-Kim Victoria, Lubner Meghan G, Somsen David, Garrett John W, Summers Ronald M, Pickhardt Perry J
Department of Radiology, The University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252.
Mallinckrodt Institute of Radiology, St. Louis, MO.
AJR Am J Roentgenol. 2023 Nov;221(5):611-619. doi: 10.2214/AJR.23.29478. Epub 2023 Jun 28.
Splenomegaly historically has been assessed on imaging by use of potentially inaccurate linear measurements. Prior work tested a deep learning artificial intelligence (AI) tool that automatically segments the spleen to determine splenic volume. The purpose of this study is to apply the deep learning AI tool in a large screening population to establish volume-based splenomegaly thresholds. This retrospective study included a primary (screening) sample of 8901 patients (4235 men, 4666 women; mean age, 56 ± 10 [SD] years) who underwent CT colonoscopy ( = 7736) or renal donor CT ( = 1165) from April 2004 to January 2017 and a secondary sample of 104 patients (62 men, 42 women; mean age, 56 ± 8 years) with end-stage liver disease who underwent contrast-enhanced CT performed as part of evaluation for potential liver transplant from January 2011 to May 2013. The automated deep learning AI tool was used for spleen segmentation, to determine splenic volumes. Two radiologists independently reviewed a subset of segmentations. Weight-based volume thresholds for splenomegaly were derived using regression analysis. Performance of linear measurements was assessed. Frequency of splenomegaly in the secondary sample was determined using weight-based volumetric thresholds. In the primary sample, both observers confirmed splenectomy in 20 patients with an automated splenic volume of 0 mL; confirmed incomplete splenic coverage in 28 patients with a tool output error; and confirmed adequate segmentation in 21 patients with low volume (< 50 mL), 49 patients with high volume (> 600 mL), and 200 additional randomly selected patients. In 8853 patients included in analysis of splenic volumes (i.e., excluding a value of 0 mL or error values), the mean automated splenic volume was 216 ± 100 [SD] mL. The weight-based volumetric threshold (expressed in milliliters) for splenomegaly was calculated as (3.01 × weight [expressed as kilograms]) + 127; for weight greater than 125 kg, the splenomegaly threshold was constant (503 mL). Sensitivity and specificity for volume-defined splenomegaly were 13% and 100%, respectively, at a true craniocaudal length of 13 cm, and 78% and 88% for a maximum 3D length of 13 cm. In the secondary sample, both observers identified segmentation failure in one patient. The mean automated splenic volume in the 103 remaining patients was 796 ± 457 mL; 84% (87/103) of patients met the weight-based volume-defined splenomegaly threshold. We derived a weight-based volumetric threshold for splenomegaly using an automated AI-based tool. The AI tool could facilitate large-scale opportunistic screening for splenomegaly.
从历史上看，脾肿大一直是通过使用可能不准确的线性测量在影像学上进行评估的。先前的研究测试了一种深度学习人工智能（AI）工具，该工具可自动分割脾脏以确定脾脏体积。本研究的目的是在大量筛查人群中应用深度学习AI工具，以建立基于体积的脾肿大阈值。这项回顾性研究包括一个主要（筛查）样本，共8901例患者（4235名男性，4666名女性；平均年龄56±10 [标准差]岁），他们在2004年4月至2017年1月期间接受了CT结肠镜检查（n = 7736）或肾供体CT（n = 1165）；以及一个次要样本，共104例患者（62名男性，42名女性；平均年龄56±8岁），这些患有终末期肝病的患者在2011年1月至2013年5月期间接受了增强CT检查，作为潜在肝移植评估的一部分。使用自动化深度学习AI工具进行脾脏分割，以确定脾脏体积。两名放射科医生独立审查了一部分分割结果。使用回归分析得出基于体重的脾肿大体积阈值。评估了线性测量的性能。使用基于体重的体积阈值确定次要样本中脾肿大的频率。在主要样本中，两名观察者均确认20例自动脾脏体积为0 mL的患者已行脾切除术；确认28例工具输出错误的患者脾脏覆盖不完整；并确认21例低体积（<50 mL）、49例高体积（>600 mL）患者以及另外200例随机选择的患者分割充分。在纳入脾脏体积分析的8853例患者中（即排除0 mL值或错误值），自动脾脏平均体积为216±100 [标准差] mL。脾肿大的基于体重的体积阈值（以毫升表示）计算为（3.01×体重[以千克表示]）+ 127；对于体重超过125 kg的患者，脾肿大阈值为常数（503 mL）。在真正的头尾长度为13 cm时，体积定义的脾肿大的敏感性和特异性分别为13%和100%，对于最大三维长度为13 cm时，敏感性和特异性分别为78%和88%。在次要样本中，两名观察者均发现一名患者分割失败。其余103例患者的自动脾脏平均体积为796±457 mL；84%（87/103）的患者达到了基于体重的体积定义的脾肿大阈值。我们使用基于AI的自动化工具得出了基于体重的脾肿大体积阈值。该AI工具可促进脾肿大的大规模机会性筛查。
