Liver-to-Spleen Volume Ratio Automatically Measured on CT Predicts Decompensation in Patients with B Viral Compensated Cirrhosis

Abstract

Objective Although the liver-to-spleen volume ratio (LSVR) based on CT reflects portal hypertension, its prognostic role in cirrhotic patients has not been proven. We evaluated the utility of LSVR, automatically measured from CT images using a deep learning algorithm, as a predictor of hepatic decompensation and transplantation-free survival in patients with hepatitis B viral (HBV)-compensated cirrhosis. Materials and Methods A deep learning algorithm was used to measure the LSVR in a cohort of 1027 consecutive patients (mean age, 50.5 years; 675 male and 352 female) with HBV-compensated cirrhosis who underwent liver CT (2007–2010). Associations of LSVR with hepatic decompensation and transplantation-free survival were evaluated using multivariable Cox proportional hazards and competing risk analyses, accounting for either the Child-Pugh score (CPS) or Model for End Stage Liver Disease (MELD) score and other variables. The risk of the liver-related events was estimated using Kaplan-Meier analysis and the Aalen-Johansen estimator. Results After adjustment for either CPS or MELD and other variables, LSVR was identified as a significant independent predictor of hepatic decompensation (hazard ratio for LSVR increase by 1, 0.71 and 0.68 for CPS and MELD models, respectively; p < 0.001) and transplantation-free survival (hazard ratio for LSVR increase by 1, 0.8 and 0.77, respectively; p < 0.001). Patients with an LSVR of < 2.9 (n = 381) had significantly higher 3-year risks of hepatic decompensation (16.7% vs. 2.5%, p < 0.001) and liver-related death or transplantation (10.0% vs. 1.1%, p < 0.001) than those with an LSVR ≥ 2.9 (n = 646). When patients were stratified according to CPS (Child-Pugh A vs. B–C) and MELD (< 10 vs. ≥ 10), an LSVR of < 2.9 was still associated with a higher risk of liver-related events than an LSVR of ≥ 2.9 for all Child-Pugh ( p ≤ 0.045) and MELD ( p ≤ 0.009) stratifications. Conclusion The LSVR measured on CT can predict hepatic decompensation and transplantation-free survival in patients with HBV-compensated cirrhosis.

Spleen volume-based non-invasive tool for predicting hepatic decompensation in people with compensated cirrhosis (CHESS1701)

“…Finally, studies on spleen volume-based prediction models for decompensation have been limited by standard statistical analyses, the absence of external validation, and/or the absence of comparisons with conventional clinical risk assessment scores. [11,12,19] Therefore, we hypothesised that spleen volume would better drive a predictive model for patients with cirrhosis at a high risk of decompensation. This study aimed to build a user-friendly non-invasive model that combines spleen volume and simple serum markers to predict the first clinical decompensation in patients with compensated cirrhosis and compare it with existing clinical models.…”

How to Clearly and Accurately Report Odds Ratio and Hazard Ratio in Diagnostic Research Studies?

“…When 28 original research studies reporting HR published in the Korean Journal of Radiology in 2020–2021 [ 11 15 32 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 ] were evaluated against a rigorous standard, 96.4% (27/28) explicitly defined the events for HR, 48.1% (13/27) unmistakably described the reference category for HR calculation for categorical variables, and only 50% (10/20) clearly described the one-unit amount for HR for continuous variables. Therefore, further improvements are required.…”

“…Figure 2 shows slightly different styles to clearly and accurately report OR or HR [ 19 41 45 ]. Examples can be found in other published articles [ 15 28 33 37 38 52 59 62 ].…”

“…(B) Modified from Kim et al Korean J Radiol 2022;23:172-179, with permission of the Korean Society of Radiology [ 41 ]. (C) Modified from Kwon et al Korean J Radiol 2021;22:1985-1995, with permission of the Korean Society of Radiology [ 45 ].…”

Looking Ahead to 2022 for the Korean Journal of Radiology

“…KJR pays close attention to external validation/testing while reviewing manuscripts, as demonstrated by some recent articles [ 7 12 13 14 ]. One particular issue that is garnering attention with respect to the introduction of AI in radiology is “opportunistic” automated body composition analyses using imaging performed for other targeted purposes such as quantitative measurements of bone mineral density, visceral and subcutaneous fat, skeletal muscle, liver fat, coronary vascular calcification, and organ size [ 12 15 16 17 18 19 ]. The labor-intensive nature of manual (or even semi-automated) body composition measurements has largely prevented their translation from the research realm to routine clinical practice or large-scale population healthcare [ 15 ].…”