Lee Sungwon, Elton Daniel C, Yang Alexander H, Koh Christopher, Kleiner David E, Lubner Meghan G, Pickhardt Perry J, Summers Ronald M
Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, 10 Center Dr, Building 10, Room 1C224D, Bethesda, MD 20892-1182 (S.L., D.C.E., R.M.S.); Liver Diseases Branch, Intramural Research Program, National Institute of Diabetes and Digestive and Kidney Diseases (A.H.Y., C.K.), and Laboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, Md (D.E.K.); and Department of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, Wis (M.G.L., P.J.P.).
Radiol Artif Intell. 2022 Aug 24;4(5):e210268. doi: 10.1148/ryai.210268. eCollection 2022 Sep.
To evaluate the performance of a deep learning (DL) model that measures the liver segmental volume ratio (LSVR) (ie, the volumes of Couinaud segments I-III/IV-VIII) and spleen volumes from CT scans to predict cirrhosis and advanced fibrosis.
For this Health Insurance Portability and Accountability Act-compliant, retrospective study, two datasets were used. Dataset 1 consisted of patients with hepatitis C who underwent liver biopsy (METAVIR F0-F4, 2000-2016). Dataset 2 consisted of patients who had cirrhosis from other causes who underwent liver biopsy (Ishak 0-6, 2001-2021). Whole liver, LSVR, and spleen volumes were measured with contrast-enhanced CT by radiologists and the DL model. Areas under the receiver operating characteristic curve (AUCs) for diagnosing advanced fibrosis (≥METAVIR F2 or Ishak 3) and cirrhosis (≥METAVIR F4 or Ishak 5) were calculated. Multivariable models were built on dataset 1 and tested on datasets 1 (hold out) and 2.
Datasets 1 and 2 consisted of 406 patients (median age, 50 years [IQR, 44-56 years]; 297 men) and 207 patients (median age, 50 years [IQR, 41-57 years]; 147 men), respectively. In dataset 1, the prediction of cirrhosis was similar between the manual versus automated measurements for spleen volume (AUC, 0.86 [95% CI: 0.82, 0.9] vs 0.85 [95% CI: 0.81, 0.89]; significantly noninferior, < .001) and LSVR (AUC, 0.83 [95% CI: 0.78, 0.87] vs 0.79 [95% CI: 0.74, 0.84]; < .001). The best performing multivariable model achieved AUCs of 0.94 (95% CI: 0.89, 0.99) and 0.79 (95% CI: 0.71, 0.87) for cirrhosis and 0.8 (95% CI: 0.69, 0.91) and 0.71 (95% CI: 0.64, 0.78) for advanced fibrosis in datasets 1 and 2, respectively.
The CT-based DL model performed similarly to radiologists. LSVR and splenic volume were predictive of advanced fibrosis and cirrhosis. CT, Liver, Cirrhosis, Computer Applications-Detection/Diagnosis © RSNA, 2022.
评估一种深度学习（DL）模型的性能，该模型可通过CT扫描测量肝脏节段体积比（LSVR）（即Couinaud I-III段/IV-VIII段的体积）和脾脏体积，以预测肝硬化和晚期纤维化。
对于这项符合《健康保险流通与责任法案》的回顾性研究，使用了两个数据集。数据集1由接受肝活检的丙型肝炎患者组成（METAVIR F0-F4，2000 - 2016年）。数据集2由因其他原因导致肝硬化且接受肝活检的患者组成（Ishak 0-6，2001 - 2021年）。放射科医生和DL模型通过增强CT测量全肝、LSVR和脾脏体积。计算诊断晚期纤维化（≥METAVIR F2或Ishak 3）和肝硬化（≥METAVIR F4或Ishak 5）的受试者操作特征曲线下面积（AUC）。在数据集1上建立多变量模型，并在数据集1（留出法）和数据集2上进行测试。
数据集1和数据集2分别包含406例患者（中位年龄50岁[四分位间距，44 - 56岁]；297例男性）和207例患者（中位年龄50岁[四分位间距，41 - 57岁]；147例男性）。在数据集1中，脾脏体积的手动测量与自动测量对肝硬化的预测相似（AUC，0.86[95%CI：0.82，0.9]对0.85[95%CI：0.81，0.89]；显著非劣效，P <.001），LSVR的预测也相似（AUC，0.83[95%CI：0.78，0.87]对0.79[95%CI：0.74，0.84]；P <.001）。表现最佳的多变量模型在数据集1和数据集中预测肝硬化的AUC分别为0.94（95%CI：0.89，0.99）和0.79（95%CI：0.71，0.87），预测晚期纤维化的AUC分别为0.8（95%CI：0.69，0.91）和0.71（95%CI：0.64，0.78）。
基于CT的DL模型表现与放射科医生相似。LSVR和脾脏体积可预测晚期纤维化和肝硬化。CT、肝脏、肝硬化、计算机应用 - 检测/诊断 ©RSNA，2022年
