An index based on deep learning–measured spleen volume on CT for the assessment of high-risk varix in B-viral compensated cirrhosis

Chul min Lee, Seung Soo Lee, Won Mook Choi, Kang Mo Kim, Yu Sub Sung, Sunho Lee, So Jung Lee, Jee Seok Yoon, Heung Il Suk

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)


Objectives: Deep learning enables an automated liver and spleen volume measurements on CT. The purpose of this study was to develop an index combining liver and spleen volumes and clinical factors for detecting high-risk varices in B-viral compensated cirrhosis. Methods: This retrospective study included 419 patients with B-viral compensated cirrhosis who underwent endoscopy and CT from 2007 to 2008 (derivation cohort, n = 239) and from 2009 to 2010 (validation cohort, n = 180). The liver and spleen volumes were measured on CT images using a deep learning algorithm. Multivariable logistic regression analysis of the derivation cohort developed an index to detect endoscopically confirmed high-risk varix. The cumulative 5-year risk of varix bleeding was evaluated with patients stratified by their index values. Results: The index of spleen volume-to-platelet ratio was devised from the derivation cohort. In the validation cohort, the cutoff index value for balanced sensitivity and specificity (> 3.78) resulted in the sensitivity of 69.4% and the specificity of 78.5% for detecting high-risk varix, and the cutoff index value for high sensitivity (> 1.63) detected all high-risk varices. The index stratified all patients into the low (index value ≤ 1.63; n = 118), intermediate (n = 162), and high (index value > 3.78; n = 139) risk groups with cumulative 5-year incidences of varix bleeding of 0%, 1.0%, and 12.0%, respectively (p <.001). Conclusion: The spleen volume-to-platelet ratio obtained using deep learning–based CT analysis is useful to detect high-risk varices and to assess the risk of varix bleeding. Key Points: • The criterion of spleen volume to platelet > 1.63 detected all high-risk varices in the validation cohort, while the absence of visible varix did not exclude all high-risk varices. • Visual varix grade ≥ 2 detected high-risk varix with a high specificity (96.5–100%). • Combining spleen volume-to-platelet ratio ≤ 1.63 and visual varix grade of 0 identified low-risk patients who had no high-risk varix and varix bleeding on 5-year follow-up.

Original languageEnglish
Pages (from-to)3355-3365
Number of pages11
JournalEuropean Radiology
Issue number5
Publication statusPublished - 2021 May

Bibliographical note

Funding Information:
This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (Grant Number, 2020R1F1A1048826) and the Bio and Medical Technology Development Program of the NRF funded by the Ministry of Science and ICT, and Future Planning (Grant Number, NRF-2016M3A7918706).

Publisher Copyright:
© 2020, European Society of Radiology.


  • Deep learning
  • Esophageal and gastric varices
  • Liver cirrhosis
  • Tomography, X-ray computed

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging


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