Fully Automated and Explainable Liver Segmental Volume Ratio and Spleen Segmentation at CT for Diagnosing Cirrhosis

Sungwon Lee; Daniel C Elton; Alexander H Yang; Christopher Koh; David E Kleiner; Meghan G Lubner; Perry J Pickhardt; Ronald M Summers

doi:10.1148/ryai.210268

Fully Automated and Explainable Liver Segmental Volume Ratio and Spleen Segmentation at CT for Diagnosing Cirrhosis

Radiol Artif Intell. 2022 Aug 24;4(5):e210268. doi: 10.1148/ryai.210268. eCollection 2022 Sep.

Authors

Sungwon Lee¹, Daniel C Elton¹, Alexander H Yang¹, Christopher Koh¹, David E Kleiner¹, Meghan G Lubner¹, Perry J Pickhardt^#¹, Ronald M Summers^#¹

Affiliation

¹ 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.).

^# Contributed equally.

Abstract

Purpose: 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.

Materials and methods: 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.

Results: 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, P < .001) and LSVR (AUC, 0.83 [95% CI: 0.78, 0.87] vs 0.79 [95% CI: 0.74, 0.84]; P < .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.

Conclusion: The CT-based DL model performed similarly to radiologists. LSVR and splenic volume were predictive of advanced fibrosis and cirrhosis.Keywords: CT, Liver, Cirrhosis, Computer Applications-Detection/Diagnosis Supplemental material is available for this article. © RSNA, 2022.

Keywords: CT; Cirrhosis; Computer Applications-Detection/Diagnosis; Liver.