ArticleJournal of clinical medicine2021
CT-Based Prediction of Liver Function and Post-PVE Hypertrophy Using an Artificial Neural Network.
Article in Journal of clinical medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 8 citations in OpenAlex.
- CT Radiomics to Predict Regenerative Biomarker Expression after Liver Venous Embolization: Proof-of-Concept in a Porcine Model.Journal of vascular and interventional radiology : JVIR · 2026Article
- Exploring radiomics as a predictor of hypertrophy of future liver remnant after portal vein embolization in biliary malignancies: a retrospective cohort study.Abdominal radiology (New York) · 2026Article
- A Multiphase CT-Based Radiomics-Clinical Model to Identify Inadequate Future Liver Remnant Hypertrophy After Portal Vein Embolization Before Major Hepatectomy.Cancer management and research · 2026Article
- Development and External Validation of a Combined Clinical-Radiomic Model for Predicting Insufficient Hypertrophy of the Future Liver Remnant following Portal Vein Embolization.Annals of surgical oncology · 2025Article
- Time to surgery is not an oncological risk factor in patients with cholangiocarcinoma undergoing curative-intent liver surgery.Scientific reports · 2024Article
- Multi-organ Radiomics-Based Prediction of Future Remnant Liver Hypertrophy Following Portal Vein Embolization.Annals of surgical oncology · 2023Article
- Basics, Epidemiology, Diagnosis, and Management of Liver Tumor.Journal of clinical medicine · 2023Article
- A Preoperative MRI-Based Radiomics-Clinicopathological Classifier to Predict the Recurrence of Pituitary Macroadenoma Within 5 Years.Frontiers in neurology · 2021Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study aimed to evaluate whether hypertrophy after portal vein embolization (PVE) and maximum liver function capacity (LiMAx) are predictable by an artificial neural network (ANN) model based on computed tomography (CT) texture features.
methodsWe report a retrospective analysis on 118 patients undergoing preoperative assessment by CT before and after PVE for subsequent extended liver resection due to a malignant tumor at RWTH Aachen University Hospital. The LiMAx test was carried out in a subgroup of 55 patients prior to PVE. Associations between CT texture features and hypertrophy as well as liver function were assessed by a multilayer perceptron ANN model.
resultsLiver volumetry showed a median hypertrophy degree of 33.9% (16.5-60.4%) after PVE. Non-response, defined as a hypertrophy grade lower than 25%, was found in 36.5% (43/118) of the cases. The ANN prediction of the hypertrophy response showed a sensitivity of 95.8%, specificity of 44.4% and overall prediction accuracy of 74.6% (
conclusionOur study shows that an ANN model based on CT texture features is able to predict the maximum liver function capacity and may be useful to assess potential hypertrophy after performing PVE.
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