ArticleScientific reports2026
Deep learning-based system to predict hepatocellular carcinoma resection volume using contrast-enhanced CT.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
Accurate calculation of liver resection volume from contrast-enhanced computed tomography images is an essential process in preoperative surgical planning for precise radical resection, which is labor-intensive, time-consuming, and prone to inter-observer variability. We propose a system based on artificial intelligence, named Liver Resection Volume Calculation with Deep learning, that can calculate the planned liver resection volume with high accuracy and efficiency. Our system is trained and tested on medical imaging scans of 990 pathology-confirmed hepatocellular carcinoma patients from two tertiary hospitals collected between January 2012 and December 2022. This system reduces the time consumed by nearly twenty folds and achieves comparable results in calculating parenchymal hepatic resection rate for variable surgery types compared to the manual process based on 3D simulation software used in the hospital currently. Its consistency with the planning results of experienced surgeons demonstrates its applicability in clinical workflow.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.