guest ::
| Home > Publications database > Detection of Esophageal Varices and Prediction of Hepatic Decompensation in Unresectable Hepatocellular Carcinoma using AI: AI Detection of Varices and Decompensation. |
| Home > Publications database > Detection of Esophageal Varices and Prediction of Hepatic Decompensation in Unresectable Hepatocellular Carcinoma using AI: AI Detection of Varices and Decompensation. |
| Journal Article | DKFZ-2026-00359 |
; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ;
2026
Elsevier Science
Amsterdam [u.a.]
Abstract: In hepatocellular carcinoma (HCC) with cirrhosis, portal hypertension (PHT) worsens outcomes. Esophagogastroduodenoscopy (EGD), current screen for esophageal varices (EVs), is invasive and delays therapy. We aimed to develop and externally validate non-invasive models to detect EV and predict hepatic decompensation (bleeding, ascites or hepatic encephalopathy), a major cause of HCC mortality, using routine contrast-enhanced CT (CECT) and clinical data.This multicenter retrospective study included 489 patients with unresectable HCC treated with Atezolizumab-Bevacizumab (AtezoBev) from five French centers, split into a development (n=279) and external validation (n=210) cohorts. Arterial-phase CECTs were processed through a Deep Learning pipeline using a foundation model (HepatoSageCT). Logistic and Cox models generated clinical models and combined models integrating the HepatoSageCT scores with key clinical variables for EVs and hepatic decompensation. Performance was assessed using AUCROC, sensitivity, specificity, concordance index and cause-specific hazard ratio.Portosystemic shunts (PSS) at imaging identified EVs with AUCROC of 0.78, increasing to 0.84 when combined with HepatoSageCT. A decision algorithm incorporating PSS and HepatoSageCT missed 4.2% of varices needing treatment, compared to 8.4% when using only PSS while missing 0% of large EVs. HepatoSageCT predicted hepatic decompensation in validation (C-Index: 0.73, hazard ratio: 3.17) with significant stratification (p<0.001) comparable to a score of ascites, splenomegaly and HepatoSageCT risk (C-Index: 0.73, hazard ratio: 3.48). Patients stratified at higher risk of decompensation by HepatoSageCT also showed significantly (p<0.001) lower overall survival (OS).HepatoSageCT scores, supplemented with clinical data, enables accurate non-invasive detection of EV in AtezoBev-treated unresectable HCC and stratifying patients based on hepatic decompensation risk, potentially assisting in reducing unnecessary endoscopies and guiding prognosis.The present study demonstrates that foundation models applied to routine CT imaging, when combined with routinely-collected features such as the presence of portosystemic shunts, can accurately predict the presence of esophageal varices and risk of hepatic and first hepatic decompensation in patients with AtezoBev-treated unresectable HCC. These findings are particularly relevant for hepatologists and oncologists, as they highlight a promising non-invasive tool for timely risk assessment in a time-sensitive patient population. While prospective validation is warranted, this approach could support more personalized management and care of patients with unresectable HCC.
Keyword(s): deep learning ; hepatic decompensation ; hepatocellular carcinoma ; portal hypertension ; portosystemic shunt ; varices
; BIOSIS Previews ; Biological Abstracts ; Clarivate Analytics Master Journal List ; Current Contents - Clinical Medicine ; Current Contents - Life Sciences ; Ebsco Academic Search ; Essential Science Indicators ; IF >= 25 ; JCR ; Nationallizenz
; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection
|
The record appears in these collections: |
