Using Artificial Intelligence to Predict Cirrhosis From Computed Tomography Scans.
Using Artificial Intelligence to Predict Cirrhosis From Computed Tomography Scans.
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DOI：
10.14309/ctg.0000000000000616
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发表时间：
2023-10-01
影响因子：
3.6
通讯作者：
Su, Grace L.
中科院分区：
文献类型：
--
作者：
Mazumder, Nikhilesh R.;Enchakalody, Binu;Zhang, Peng;Su, Grace L.
未确诊的肝硬变仍然是一个严重的问题。在这项研究中，我们开发并测试了一种自动肝脏分割工具，以预测在一组配对的肝脏活检和计算机断层扫描(CT)的患者中是否存在肝硬变。我们在Morphology数据库中使用了1,590次CT扫描的队列，使用3D-U-Net和Google的DeeplLabv3+训练了自动肝脏分割模型。然后，根据2004年1月至2012年1月在6个月内进行肝脏活检和CT检查的慢性肝病患者的外部测试队列，自动计算成像特征。使用梯度增强决策树，我们开发了多变量模型来预测组织学肝硬变的存在，并用5倍交叉验证的c统计量进行评估。我们的队列中有351名患者，其中96名患者患有肝硬变。在全部队列中，72人是肝移植后的。纤维蛋白原(FIB)-4和肝脏形态组学均表现良好，分别为0.76(95%可信区间0.70~0.81)和0.71(95%可信区间0.65~0.76)(P=0.2)。然而，与FIB-4单独使用相比，肝脏形态组学与实验值或肝脏形态组学与实验室和人口统计学数据的组合导致了显著改善的性能，在接受操作特征下的面积分别为0.84%(0.800.89)和0.85%(0.810.90)(P<；0.001)。在亚组分析中，我们还检查了未进行肝移植的患者的表现，发现FIB-4也有类似的增强。这项原理验证研究表明，在CT扫描中自动提取的特征可以与经典的电子病历数据相结合，以提高对肝病患者肝硬变的预测。这一工具可以用于移植前和移植后的患者，并有可能提高我们检测未诊断的肝硬变的能力。
Undiagnosed cirrhosis remains a significant problem. In this study, we developed and tested an automated liver segmentation tool to predict the presence of cirrhosis in a population of patients with paired liver biopsy and computed tomography (CT) scans. We used a cohort of 1,590 CT scans within the Morphomics database to train an automated liver segmentation model using 3D-U-Net and Google's DeeplLabv3+. Imaging features were then automatically calculated from an external test cohort of patients with chronic liver disease who had a paired liver biopsy and CT within 6 months of each other in January 2004–2012. Using gradient boosting decision trees, we developed multivariate models to predict the presence of histologic cirrhosis and evaluated with 5-fold cross-validated c-statistic. Our cohort had 351 patients; 96 patients had cirrhosis. Of the total cohort, 72 were postliver transplant. Both fibrosis (FIB)-4 and liver morphomics alone performed equally well with area under the receiving operating characteristics of 0.76 (95% confidence interval 0.70–0.81) and 0.71 (95% confidence interval 0.65–0.76), respectively (P = 0.2). However, the combination of liver morphomics with laboratory values or liver morphomics with laboratory and demographic data resulted in significant improved performance with area under the receiving operating characteristics of 0.84 (0.80–0.89) and 0.85 (0.81–0.90), respectively, compared with FIB-4 alone (P < 0.001). In a subgroup analysis, we also examined performance in patients without liver transplantation and saw similar augmentation of FIB-4. This proof-of-principle study demonstrates that automatically extracted features within CT scans can be combined with classic electronic medical record data to improve the prediction of cirrhosis in patients with liver disease. This tool may be used in both pretransplant and posttransplant patients and has the potential to improve our ability to detect undiagnosed cirrhosis.
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